diff --git a/.DS_Store b/.DS_Store
index 3106d22..66e5cab 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/Handle_emg_data.py b/Handle_emg_data.py
index 688a615..c9a5a9d 100644
--- a/Handle_emg_data.py
+++ b/Handle_emg_data.py
@@ -38,7 +38,7 @@ class CSV_handler:
         self.nr_subjects = nr_subjects
         self.nr_sessions = nr_sessions
         # Dict with keys equal subject numbers and values equal to its respective datacontainer 
-        self.data_container_dict = {i: None for i in range(nr_subjects)}  
+        self.data_container_dict = {i+1: None for i in range(nr_subjects)}  
         # String describing which type of data is stored in the object 
         self.data_type = None   
 
diff --git a/Neural_Network_Analysis.py b/Neural_Network_Analysis.py
index 39673d2..8133943 100644
--- a/Neural_Network_Analysis.py
+++ b/Neural_Network_Analysis.py
@@ -56,12 +56,12 @@ def prepare_datasets_percentsplit(X, y, shuffle_vars, validation_size=0.2, test_
 
     return X_train, X_validation, X_test, y_train, y_validation, y_test
 
-# Takes in data, labels, and session_lengths and splits it into train and test sets by session_index
-# Input: Data, labels, session_lengths, test_session_index
-# Ouput: X_train, X_test, y_train, y_test
+# Takes in data and labels, and splits it into train and test sets by session
+# Input: Data, labels, session_lengths and test_session_index
+# Ouput: X_train, X_validation, X_test, y_train, y_validation, y_test
 def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_subjects=5):
 
-    session_lengths = session_lengths.tolist()
+    session_lengths = list(session_lengths)
 
     subject_starting_index = 0
     start_test_index = subject_starting_index + sum(session_lengths[0][:test_session_index-1])
@@ -138,6 +138,49 @@ def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_su
 
     return X_train, X_test, y_train, y_test
 
+# NOT FUNCTIONAL
+def prepare_datasets_new(test_session_indexes:list, X, y, session_lengths, nr_subjects=5, nr_sessions=4):
+
+    X_list = []
+    y_list = []
+   
+    for session_i in range(nr_sessions):
+        X_session_list = []
+        y_session_list = []
+        for subject_i in range(nr_subjects):
+        
+            session_data_X = X[0:session_lengths[subject_i][session_i]]
+            session_data_y = y[0:session_lengths[subject_i][session_i]]
+            if session_i > 0:
+                start_index = X_list[session_i-1].shape[0]
+                session_data_X = X[start_index : start_index + session_lengths[subject_i][session_i]]
+                session_data_y = y[start_index : start_index + session_lengths[subject_i][session_i]]
+            X_session_list.append(session_data_X)
+            y_session_list.append(session_data_y)
+        X_list.append(np.concatenate(X_session_list))
+        y_list.append(np.concatenate(y_session_list))
+
+    X_test = []
+    y_test = []
+    X_train = []
+    y_train = []
+
+
+    for i in range(nr_sessions):
+        if i in test_session_indexes:
+            X_test.append(X_list[i])
+            y_test.append(y_list[i])
+        else:
+            X_train.append(X_list[i])
+            y_train.append(y_list[i])
+    
+    X_test = np.concatenate(X_test)
+    y_test = np.concatenate(y_test)
+    X_train = np.concatenate(X_train)
+    y_train = np.concatenate(y_train)
+
+    return X_train, X_test, y_train, y_test
+
 # Trains the model 
 # Input: Keras.model, batch_size, nr epochs, training, and validation data
 # Ouput: History
@@ -340,13 +383,29 @@ def get_session_info(session_lengths_soft, session_lengths_hard):
 # Reduces the size of the train and test set with values [0.0, 1.0]
 # Input: Data sets, how much to reduce train set, how much to reduce test set with 
 # Output: Reduced data sets
-def reduce_data_set_sizes(X_train, X_test, y_train, y_test, train_reduction=0.5, test_reduction=0):
-    train_keep = X_train.shape[0] * (1 - train_reduction)
-    test_keep = X_test.shape[0] * (1 - test_reduction)
-    X_train = X_train[:train_keep]
-    y_train = y_train[:train_keep]
-    X_test = X_test[:test_keep]
-    y_test = y_test[:test_keep]
+def reduce_data_set_sizes(X_train, X_test, y_train, y_test, train_reduction=0.5, test_reduction=0, nr_subjects=5):
+    
+    X_train = np.array_split(X_train, nr_subjects)
+    y_train = np.array_split(y_train, nr_subjects)
+    X_test = np.array_split(X_test, nr_subjects)
+    y_test = np.array_split(y_test, nr_subjects)
+    
+    train_keep = int(X_train[0].shape[0] * (1 - train_reduction))
+    test_keep = int(X_test[0].shape[0] * (1 - test_reduction))
+
+    for i in range(nr_subjects):
+        #print(len(X_train[i]))
+        X_train[i] = X_train[i][:train_keep]
+        y_train[i] = y_train[i][:train_keep]
+        X_test[i] = X_test[i][:test_keep]
+        y_test[i] = y_test[i][:test_keep]
+        #print(len(X_train[i]))
+    
+    X_train = np.concatenate(X_train, axis=0)
+    y_train = np.concatenate(y_train, axis=0)
+    X_test = np.concatenate(X_test, axis=0)
+    y_test = np.concatenate(y_test, axis=0)
+
     return X_train, X_test, y_train, y_test
 
 # ----- PLOTS ------
@@ -663,20 +722,20 @@ def plot_comp_SoftHard_single(X_soft, y_soft, X_hard, y_hard, session_lengths_so
     fig, axs = plt.subplots(2, sharey=True)
     plt.ylim(0, 1)
     plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
-    fig.suptitle('Model training and validation with SOFT/HARD data', fontsize=16)
+    fig.suptitle('Model training (1x session) and validation (3x session) with Natural/Strong typing behavior', fontsize=16)
 
-    axs[0].plot(train_dict['SOFT'], ':', label='CNN_1D SOFT')
-    axs[0].plot(train_dict['HARD'], '--', label='CNN_1D HARD')
+    axs[0].plot(train_dict['SOFT'], ':', label='CNN_1D Natural')
+    axs[0].plot(train_dict['HARD'], '--', label='CNN_1D Strong')
     axs[0].set_title('Training accuracy')
     
-    axs[1].plot(val_dict['SOFT'], ':', label='CNN_1D SOFT')
-    axs[1].plot(val_dict['HARD'], '--', label='CNN_1D HARD')
+    axs[1].plot(val_dict['SOFT'], ':', label='CNN_1D Natural')
+    axs[1].plot(val_dict['HARD'], '--', label='CNN_1D Strong')
     axs[1].set_title('Validation accuracy')
     
     for ax in axs.flat:
         ax.set(xlabel='Epochs', ylabel='Accuracy')
     
-    plt.legend(bbox_to_anchor=(1.05, 1.5), title='Models used\n', loc='center left')
+    plt.legend(bbox_to_anchor=(1.05, 1.5), title='Typing behavior evaluated\n', loc='center left')
     plt.style.use('seaborn-dark-palette') 
     plt.show()
 
@@ -751,24 +810,133 @@ def plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, s
     fig, axs = plt.subplots(2, sharey=True)
     plt.ylim(0, 1)
     plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
-    fig.suptitle('Model training and validation with SOFT/HARD data', fontsize=16)
+    fig.suptitle('Model training (3x session) and validation (1x session) with Natural/Strong typing behavior', fontsize=16)
 
-    axs[0].plot(train_dict['SOFT'], ':', label='CNN_1D SOFT')
-    axs[0].plot(train_dict['HARD'], '--', label='CNN_1D HARD')
+    axs[0].plot(train_dict['SOFT'], ':', label='CNN_1D Natural')
+    axs[0].plot(train_dict['HARD'], '--', label='CNN_1D Strong')
     axs[0].set_title('Training accuracy')
     
-    axs[1].plot(val_dict['SOFT'], ':', label='CNN_1D SOFT')
-    axs[1].plot(val_dict['HARD'], '--', label='CNN_1D HARD')
+    axs[1].plot(val_dict['SOFT'], ':', label='CNN_1D Natural')
+    axs[1].plot(val_dict['HARD'], '--', label='CNN_1D Strong')
     axs[1].set_title('Validation accuracy')
     
     for ax in axs.flat:
         ax.set(xlabel='Epochs', ylabel='Accuracy')
     
-    plt.legend(bbox_to_anchor=(1.05, 1.5), title='Models used\n', loc='center left')
+    plt.legend(bbox_to_anchor=(1.05, 1.5), title='Typing behavior evaluated\n', loc='center left')
     plt.style.use('seaborn-dark-palette') 
     plt.show()
 
 
+# Plots training and validation history for CNN_1D network with SOFT and HARD data (VAL, two data sets)
+# Input: SOFT and HARD raw data, respective session_lengths, *details
+# Output: None -> plot
+def plot_comp_val_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, nr_sessions, batch_size=64, epochs=30):
+    #'''
+    #train_dict = {'SOFT':[], 'HARD':[], 'SOFT_1':[], 'HARD_1':[]}
+    val_dict = {'SOFT':[], 'HARD':[], 'SOFT_1':[], 'HARD_1':[]}
+    
+    for i in range(nr_sessions):
+        # Prepare data
+        X_train_soft, X_val_soft, y_train_soft, y_val_soft = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, i)
+        X_train_hard, X_val_hard, y_train_hard, y_val_hard = prepare_datasets_sessions(X_hard, y_hard, session_lengths_hard, i)
+        X_train_soft = np.reshape(X_train_soft, (X_train_soft.shape[0], 208, 1))
+        X_val_soft = np.reshape(X_val_soft, (X_val_soft.shape[0], 208, 1))
+        X_train_hard = np.reshape(X_train_hard, (X_train_hard.shape[0], 208, 1))
+        X_val_hard = np.reshape(X_val_hard, (X_val_hard.shape[0], 208, 1))
+        
+        # CNN_1D SOFT
+        model_CNN_1D = CNN_1D(input_shape=(208, 1))
+        CNN_1D_h = train(model_CNN_1D, X_train_soft, y_train_soft, 1, batch_size=batch_size, epochs=epochs, 
+                                                            X_validation=X_val_soft, y_validation=y_val_soft)
+        #train_dict['SOFT'].append(list(CNN_1D_h.history['accuracy']))
+        val_dict['SOFT'].append(list(CNN_1D_h.history['val_accuracy']))
+        del model_CNN_1D
+        K.clear_session()
+
+        # CNN_1D HARD
+        model_CNN_1D = CNN_1D(input_shape=(208, 1))
+        CNN_1D_h = train(model_CNN_1D, X_train_hard, y_train_hard, 1, batch_size=batch_size, epochs=epochs, 
+                                                            X_validation=X_val_hard, y_validation=y_val_hard)
+        #train_dict['HARD'].append(list(CNN_1D_h.history['accuracy']))
+        val_dict['HARD'].append(list(CNN_1D_h.history['val_accuracy']))
+        del model_CNN_1D
+        K.clear_session()
+
+        # ------ Single:
+        
+        # CNN_1D SOFT
+        model_CNN_1D = CNN_1D(input_shape=(208, 1))
+        CNN_1D_h = train(model_CNN_1D, X_val_soft, y_val_soft, 1, batch_size=batch_size, epochs=epochs, 
+                                                            X_validation=X_train_soft, y_validation=y_train_soft)
+        #train_dict['SOFT_1'].append(list(CNN_1D_h.history['accuracy']))
+        val_dict['SOFT_1'].append(list(CNN_1D_h.history['val_accuracy']))
+        del model_CNN_1D
+        K.clear_session()
+
+        # CNN_1D HARD
+        model_CNN_1D = CNN_1D(input_shape=(208, 1))
+        CNN_1D_h = train(model_CNN_1D, X_val_hard, y_val_hard, 1, batch_size=batch_size, epochs=epochs, 
+                                                            X_validation=X_train_hard, y_validation=y_train_hard)
+        #train_dict['HARD_1'].append(list(CNN_1D_h.history['accuracy']))
+        val_dict['HARD_1'].append(list(CNN_1D_h.history['val_accuracy']))
+        del model_CNN_1D
+        K.clear_session()
+
+
+    # Averaging out session training for each network
+    #for key in train_dict:
+    #    train_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*train_dict[key])))
+    for key in val_dict:
+        val_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*val_dict[key])))
+
+
+    '''
+    train_dict = {'SOFT': [0.1, 0.7, 0.5, 0.69],
+                'HARD': [0.55, 0.9, 0.3, 0.92]}
+    val_dict = {'SOFT': [0.34, 0.85, 0.41, 0.74],  
+                'HARD': [0.63, 0.99, 0.49, 0.88]}
+    '''
+    '''
+    # Log data stream to CSV
+    csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_3/soft_hard_comparison_acc_data.csv'
+    with open(csv_path, 'w') as csv_file:
+        writer = csv.writer(csv_file)
+        writer.writerow(['soft_train_acc', 'hard_train_acc', 'soft_val_acc', 'hard_val_acc'])
+        data = zip(*train_dict.values(), *val_dict.values())
+        writer.writerows(data)
+        csv_file.close()
+    
+    # Log best results to CSV
+    csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_3/soft_hard_comparison_best.csv'
+    with open(csv_path, 'w') as csv_file:
+        writer = csv.writer(csv_file)
+        writer.writerow(['soft_train_best', 'hard_train_best', 'soft_val_best', 'hard_val_best'])
+        writer.writerow( [np.max(train_dict.get('SOFT')), np.max(train_dict.get('HARD')), np.max(val_dict.get('SOFT')), np.max(val_dict.get('HARD'))] )
+        csv_file.close()
+    '''
+
+    # Plot:
+    fig, axs = plt.subplots(2, sharey=True)
+    plt.ylim(0, 1)
+    plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
+    fig.suptitle('Model training and validation with Natural/Strong typing behavior', fontsize=16)
+
+    axs[0].plot(val_dict['SOFT'], ':', label='CNN_1D Natural')
+    axs[0].plot(val_dict['HARD'], '--', label='CNN_1D Strong')
+    axs[0].set_title('Validation accuracy (3 session training)')
+    
+    axs[1].plot(val_dict['SOFT_1'], ':', label='CNN_1D Natural')
+    axs[1].plot(val_dict['HARD_1'], '--', label='CNN_1D Strong')
+    axs[1].set_title('Validation accuracy (1 session training)')
+    
+    for ax in axs.flat:
+        ax.set(xlabel='Epochs', ylabel='Accuracy')
+    
+    plt.legend(bbox_to_anchor=(1.05, 1.5), title='Typing behavior evaluated\n', loc='center left')
+    plt.style.use('seaborn-dark-palette') 
+    plt.show()
+
 # ----- MODELS ------
 
 # Creates a keras.model with focus on LSTM layers
@@ -845,7 +1013,7 @@ if __name__ == "__main__":
     NR_SUBJECTS = 5
     NR_SESSIONS = 4
     BATCH_SIZE = 64
-    EPOCHS = 10
+    EPOCHS = 30
 
     TEST_SESSION_NR = 4
     VERBOSE = 1
@@ -858,28 +1026,32 @@ if __name__ == "__main__":
         # y_train.shape = (2806-y_test, nr_subjects)
         # y_test.shape = (y_test(from session nr. ?), nr_subjects)
 
-    X_train, X_test, y_train, y_test = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, TEST_SESSION_NR)
-    
+    #X_val, X_train, y_val, y_train = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, TEST_SESSION_NR)
+    #X_train, X_val, y_train, y_val = reduce_data_set_sizes(X_train, X_val, y_train, y_val, train_reduction=0.5, test_reduction=0)
+    #print(X_soft.shape, y_soft.shape)
+    #X_train, X_val, y_train, y_val = prepare_datasets_new([0, 1], X_soft, y_soft, session_lengths_soft)
+    #print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)
+
 
     # ----- Make model ------
     #model_GRU = GRU(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
     #model_LSTM = LSTM(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
-    model_CNN_1D = CNN_1D(input_shape=(208, 1)) # (timestep, 13*16 MFCC coefficients)
+    #model_CNN_1D = CNN_1D(input_shape=(208, 1)) # (timestep, 13*16 MFCC coefficients)
     
-    model_CNN_1D.summary()
     #model_GRU.summary()
     #model_LSTM.summary()
-    
+    #model_CNN_1D.summary()
 
     # ----- Train network ------
     #history_GRU = train(model_GRU, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
     #history_LSTM = train(model_LSTM, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
-    history_CNN_1D = train( model_CNN_1D, np.reshape(X_train, (X_train.shape[0], 208, 1)), 
-                            y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
+    #history_CNN_1D = train( model_CNN_1D, np.reshape(X_train, (X_train.shape[0], 208, 1)), 
+    #                        y_train, X_validation=np.reshape(X_val, (X_val.shape[0], 208, 1)), y_validation=y_val, verbose=VERBOSE, 
+    #                        batch_size=BATCH_SIZE, epochs=EPOCHS)
     
 
     # ----- Plot train accuracy/error -----
-    plot_train_history(history_CNN_1D)
+    #plot_train_history(history_CNN_1D, val_data=True)
 
 
     # ----- Evaluate model on test set ------
@@ -957,7 +1129,7 @@ if __name__ == "__main__":
 
     #plot_comp_spread_single(X, y, session_lengths, NR_SESSIONS, epochs=30)
     #plot_comp_accuracy_single(X_soft, y_soft, session_lengths_soft, NR_SESSIONS, epochs=30)
-    #plot_comp_SoftHard_single(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
+    plot_comp_val_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
 
     #plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
 
diff --git a/Present_data.py b/Present_data.py
index 52e4f0e..1ad2094 100644
--- a/Present_data.py
+++ b/Present_data.py
@@ -239,6 +239,94 @@ def mfcc_all_emg_plots(csv_handler:CSV_handler):
 
     plot_all_emg_mfcc(feat_list, label_list)
 
+# Prints (and logs) max, min, mean, EMS and median of EMG data
+# Input: CSV_handler
+# Output: None --> Print
+def log_emg_characteristics(csv_handler:CSV_handler):
+
+    min_values = []
+    max_values = []
+    mean_list = []
+    RMS_list = [] 
+    median_list = []
+
+    if csv_handler.data_type == 'soft':
+
+        for subject_container in csv_handler.data_container_dict.values():
+            min_values_sub = []
+            max_values_sub = []
+            mean_list_sub = []
+            RMS_list_sub = []  
+            median_list_sub = []
+            for session_dict in subject_container.dict_list:
+                for emg_list in session_dict.values():
+                    for emg_df in emg_list:
+
+                        df = emg_df.iloc[:,1]
+                        min_values_sub.append(df.min())
+                        max_values_sub.append(df.max())
+                        mean_list_sub.append(df.abs().mean())
+                        RMS_list_sub.append(np.sqrt(np.mean(np.square(df.to_numpy()))))
+                        median_list_sub.append(df.abs().median())
+
+                        min_values.append(df.min())
+                        max_values.append(df.max())
+                        mean_list.append(df.abs().mean())
+                        RMS_list.append(np.sqrt(np.mean(np.square(df.to_numpy()))))
+                        median_list.append(df.abs().median())
+            
+            subject_nr = subject_container.subject_nr
+            #print('\n')
+            print('Natural typing behavior, subject {}, minimum EMG value:'.format(subject_nr), min(min_values_sub))
+            print('Natural typing behavior, subject {}, maximum EMG value:'.format(subject_nr), max(max_values_sub))
+            print('Natural typing behavior, subject {}, mean EMG value:'.format(subject_nr), np.mean(mean_list_sub))
+            print('Natural typing behavior, subject {}, RMS EMG value:'.format(subject_nr), np.sqrt(np.mean(np.square(RMS_list_sub))))
+            print('Natural typing behavior, subject {}, median EMG value:'.format(subject_nr), np.median(median_list_sub))
+            print('\n')
+    
+    elif csv_handler.data_type == 'hard':
+
+        for subject_container in csv_handler.data_container_dict.values():
+            min_values_sub = []
+            max_values_sub = []
+            mean_list_sub = []
+            RMS_list_sub = []  
+            median_list_sub = []
+            for session_dict in subject_container.dict_list:
+                for emg_list in session_dict.values():
+                    for emg_df in emg_list:
+
+                        df = emg_df.iloc[:,1]
+                        min_values_sub.append(df.min())
+                        max_values_sub.append(df.max())
+                        mean_list_sub.append(df.abs().mean())
+                        RMS_list_sub.append(np.sqrt(np.mean(np.square(df.to_numpy()))))
+                        median_list_sub.append(df.abs().median())
+
+                        min_values.append(df.min())
+                        max_values.append(df.max())
+                        mean_list.append(df.abs().mean())
+                        RMS_list.append(np.sqrt(np.mean(np.square(df.to_numpy()))))
+                        median_list.append(df.abs().median())
+            
+            subject_nr = subject_container.subject_nr
+            #print('\n')
+            print('Strong typing behavior, subject {}, minimum EMG value:'.format(subject_nr), min(min_values_sub))
+            print('Strong typing behavior, subject {}, maximum EMG value:'.format(subject_nr), max(max_values_sub))
+            print('Strong typing behavior, subject {}, mean EMG value:'.format(subject_nr), np.mean(mean_list_sub))
+            print('Strong typing behavior, subject {}, RMS EMG value:'.format(subject_nr), np.sqrt(np.mean(np.square(RMS_list_sub))))
+            print('Strong typing behavior, subject {}, median EMG value:'.format(subject_nr), np.median(median_list_sub))
+            print('\n')
+
+    else:
+        raise Exception('Not available data type')
+    
+    print(min_values)
+    print(max_values)
+    print(mean_list)
+    print(RMS_list)
+    print(median_list)
+    
 
 # MAIN: ------------------------------------------------------------------------: 
 
@@ -252,13 +340,16 @@ if __name__ == "__main__":
     JSON_FILE_SOFT = 'mfcc_data_soft.json'
     JSON_FILE_HARD = 'mfcc_data_hard.json'
 
-
     csv_handler = CSV_handler(NR_SUBJECTS, NR_SESSIONS)
     dict = csv_handler.load_data('soft', soft_dir_name)
     
-    nn_handler = NN_handler(csv_handler)
-    nn_handler.store_mfcc_samples()
-    nn_handler.save_json_mfcc(JSON_FILE_SOFT)
+
+    
+    
+
+    #nn_handler = NN_handler(csv_handler)
+    #nn_handler.store_mfcc_samples()
+    #nn_handler.save_json_mfcc(JSON_FILE_SOFT)
 
 
 
diff --git a/__pycache__/Handle_emg_data.cpython-36.pyc b/__pycache__/Handle_emg_data.cpython-36.pyc
index 11a9424..668108b 100644
Binary files a/__pycache__/Handle_emg_data.cpython-36.pyc and b/__pycache__/Handle_emg_data.cpython-36.pyc differ
diff --git a/__pycache__/Handle_emg_data.cpython-38.pyc b/__pycache__/Handle_emg_data.cpython-38.pyc
index ed344df..d848663 100644
Binary files a/__pycache__/Handle_emg_data.cpython-38.pyc and b/__pycache__/Handle_emg_data.cpython-38.pyc differ
diff --git a/__pycache__/Signal_prep.cpython-36.pyc b/__pycache__/Signal_prep.cpython-36.pyc
index 167bd9a..375f064 100644
Binary files a/__pycache__/Signal_prep.cpython-36.pyc and b/__pycache__/Signal_prep.cpython-36.pyc differ
diff --git a/test_console_log.txt b/test_console_log.txt
new file mode 100644
index 0000000..7c43f6b
--- /dev/null
+++ b/test_console_log.txt
@@ -0,0 +1,982 @@
+2021-07-23 15:57:31.091976: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations:  SSE4.1 SSE4.2
+To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
+2021-07-23 15:57:31.092963: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 8. Tune using inter_op_parallelism_threads for best performance.
+Data succesfully loaded!
+Data succesfully loaded!
+Train on 2107 samples, validate on 699 samples
+Epoch 1/30
+
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2107/2107 [==============================] - 2s 751us/sample - loss: 1.7204 - accuracy: 0.4371 - val_loss: 0.7785 - val_accuracy: 0.7310
+Epoch 2/30
+
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2107/2107 [==============================] - 1s 392us/sample - loss: 0.7364 - accuracy: 0.7295 - val_loss: 0.5169 - val_accuracy: 0.8255
+Epoch 3/30
+
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2107/2107 [==============================] - 1s 388us/sample - loss: 0.4797 - accuracy: 0.8386 - val_loss: 0.4320 - val_accuracy: 0.8641
+Epoch 4/30
+
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2107/2107 [==============================] - 1s 401us/sample - loss: 0.3522 - accuracy: 0.8851 - val_loss: 0.3540 - val_accuracy: 0.8927
+Epoch 5/30
+
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2107/2107 [==============================] - 1s 399us/sample - loss: 0.2821 - accuracy: 0.9112 - val_loss: 0.3406 - val_accuracy: 0.8913
+Epoch 6/30
+
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2107/2107 [==============================] - 1s 407us/sample - loss: 0.2471 - accuracy: 0.9250 - val_loss: 0.2728 - val_accuracy: 0.9185
+Epoch 7/30
+
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2107/2107 [==============================] - 1s 413us/sample - loss: 0.1964 - accuracy: 0.9506 - val_loss: 0.2605 - val_accuracy: 0.9127
+Epoch 8/30
+
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2107/2107 [==============================] - 1s 410us/sample - loss: 0.1756 - accuracy: 0.9535 - val_loss: 0.2972 - val_accuracy: 0.8970
+Epoch 9/30
+
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2107/2107 [==============================] - 1s 411us/sample - loss: 0.1560 - accuracy: 0.9559 - val_loss: 0.2376 - val_accuracy: 0.9242
+Epoch 10/30
+
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2107/2107 [==============================] - 1s 403us/sample - loss: 0.1384 - accuracy: 0.9620 - val_loss: 0.2478 - val_accuracy: 0.9256
+Epoch 11/30
+
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2107/2107 [==============================] - 1s 406us/sample - loss: 0.1162 - accuracy: 0.9682 - val_loss: 0.2684 - val_accuracy: 0.9027
+Epoch 12/30
+
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2107/2107 [==============================] - 1s 394us/sample - loss: 0.1059 - accuracy: 0.9701 - val_loss: 0.2309 - val_accuracy: 0.9256
+Epoch 13/30
+
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2107/2107 [==============================] - 1s 389us/sample - loss: 0.0937 - accuracy: 0.9786 - val_loss: 0.2181 - val_accuracy: 0.9328
+Epoch 14/30
+
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2107/2107 [==============================] - 1s 400us/sample - loss: 0.0883 - accuracy: 0.9810 - val_loss: 0.1958 - val_accuracy: 0.9371
+Epoch 15/30
+
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2107/2107 [==============================] - 1s 403us/sample - loss: 0.0796 - accuracy: 0.9796 - val_loss: 0.1924 - val_accuracy: 0.9399
+Epoch 16/30
+
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2107/2107 [==============================] - 1s 400us/sample - loss: 0.0794 - accuracy: 0.9815 - val_loss: 0.1995 - val_accuracy: 0.9342
+Epoch 17/30
+
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2107/2107 [==============================] - 1s 393us/sample - loss: 0.0693 - accuracy: 0.9801 - val_loss: 0.2187 - val_accuracy: 0.9227
+Epoch 18/30
+
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2107/2107 [==============================] - 1s 395us/sample - loss: 0.0663 - accuracy: 0.9829 - val_loss: 0.1948 - val_accuracy: 0.9413
+Epoch 19/30
+
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2107/2107 [==============================] - 1s 388us/sample - loss: 0.0544 - accuracy: 0.9886 - val_loss: 0.2022 - val_accuracy: 0.9342
+Epoch 20/30
+
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2107/2107 [==============================] - 1s 392us/sample - loss: 0.0576 - accuracy: 0.9853 - val_loss: 0.2090 - val_accuracy: 0.9299
+Epoch 21/30
+
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2107/2107 [==============================] - 1s 390us/sample - loss: 0.0505 - accuracy: 0.9896 - val_loss: 0.2088 - val_accuracy: 0.9270
+Epoch 22/30
+
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2107/2107 [==============================] - 1s 387us/sample - loss: 0.0483 - accuracy: 0.9877 - val_loss: 0.2158 - val_accuracy: 0.9256
+Epoch 23/30
+
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2107/2107 [==============================] - 1s 385us/sample - loss: 0.0460 - accuracy: 0.9910 - val_loss: 0.2154 - val_accuracy: 0.9285
+Epoch 24/30
+
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2107/2107 [==============================] - 1s 387us/sample - loss: 0.0464 - accuracy: 0.9900 - val_loss: 0.1935 - val_accuracy: 0.9413
+Epoch 25/30
+
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2107/2107 [==============================] - 1s 383us/sample - loss: 0.0444 - accuracy: 0.9891 - val_loss: 0.1751 - val_accuracy: 0.9471
+Epoch 26/30
+
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2107/2107 [==============================] - 1s 390us/sample - loss: 0.0433 - accuracy: 0.9896 - val_loss: 0.1957 - val_accuracy: 0.9413
+Epoch 27/30
+
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2107/2107 [==============================] - 1s 388us/sample - loss: 0.0354 - accuracy: 0.9929 - val_loss: 0.1875 - val_accuracy: 0.9442
+Epoch 28/30
+
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2107/2107 [==============================] - 1s 390us/sample - loss: 0.0354 - accuracy: 0.9910 - val_loss: 0.1831 - val_accuracy: 0.9499
+Epoch 29/30
+
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2107/2107 [==============================] - 1s 384us/sample - loss: 0.0328 - accuracy: 0.9934 - val_loss: 0.2007 - val_accuracy: 0.9399
+Epoch 30/30
+
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2107/2107 [==============================] - 1s 385us/sample - loss: 0.0307 - accuracy: 0.9929 - val_loss: 0.1762 - val_accuracy: 0.9514
+Train on 2327 samples, validate on 787 samples
+Epoch 1/30
+
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2327/2327 [==============================] - 1s 638us/sample - loss: 2.0124 - accuracy: 0.3661 - val_loss: 1.0333 - val_accuracy: 0.6048
+Epoch 2/30
+
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2327/2327 [==============================] - 1s 391us/sample - loss: 0.9764 - accuracy: 0.6227 - val_loss: 0.7147 - val_accuracy: 0.7446
+Epoch 3/30
+
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2327/2327 [==============================] - 1s 392us/sample - loss: 0.6692 - accuracy: 0.7503 - val_loss: 0.5211 - val_accuracy: 0.8272
+Epoch 4/30
+
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2327/2327 [==============================] - 1s 386us/sample - loss: 0.5038 - accuracy: 0.8208 - val_loss: 0.4589 - val_accuracy: 0.8475
+Epoch 5/30
+
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2327/2327 [==============================] - 1s 389us/sample - loss: 0.3988 - accuracy: 0.8642 - val_loss: 0.3503 - val_accuracy: 0.8907
+Epoch 6/30
+
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2327/2327 [==============================] - 1s 391us/sample - loss: 0.3223 - accuracy: 0.8865 - val_loss: 0.3176 - val_accuracy: 0.9085
+Epoch 7/30
+
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2327/2327 [==============================] - 1s 388us/sample - loss: 0.2880 - accuracy: 0.9029 - val_loss: 0.2877 - val_accuracy: 0.9149
+Epoch 8/30
+
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2327/2327 [==============================] - 1s 394us/sample - loss: 0.2393 - accuracy: 0.9265 - val_loss: 0.2605 - val_accuracy: 0.9250
+Epoch 9/30
+
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2327/2327 [==============================] - 1s 389us/sample - loss: 0.2025 - accuracy: 0.9334 - val_loss: 0.2186 - val_accuracy: 0.9428
+Epoch 10/30
+
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2327/2327 [==============================] - 1s 396us/sample - loss: 0.1790 - accuracy: 0.9446 - val_loss: 0.2110 - val_accuracy: 0.9454
+Epoch 11/30
+
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2327/2327 [==============================] - 1s 398us/sample - loss: 0.1523 - accuracy: 0.9557 - val_loss: 0.1834 - val_accuracy: 0.9492
+Epoch 12/30
+
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2327/2327 [==============================] - 1s 392us/sample - loss: 0.1453 - accuracy: 0.9592 - val_loss: 0.1772 - val_accuracy: 0.9530
+Epoch 13/30
+
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2327/2327 [==============================] - 1s 390us/sample - loss: 0.1318 - accuracy: 0.9643 - val_loss: 0.1660 - val_accuracy: 0.9593
+Epoch 14/30
+
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2327/2327 [==============================] - 1s 386us/sample - loss: 0.1106 - accuracy: 0.9691 - val_loss: 0.1473 - val_accuracy: 0.9606
+Epoch 15/30
+
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2327/2327 [==============================] - 1s 395us/sample - loss: 0.1004 - accuracy: 0.9772 - val_loss: 0.1423 - val_accuracy: 0.9619
+Epoch 16/30
+
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2327/2327 [==============================] - 1s 388us/sample - loss: 0.1013 - accuracy: 0.9734 - val_loss: 0.1477 - val_accuracy: 0.9593
+Epoch 17/30
+
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2327/2327 [==============================] - 1s 415us/sample - loss: 0.0900 - accuracy: 0.9802 - val_loss: 0.1340 - val_accuracy: 0.9606
+Epoch 18/30
+
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2327/2327 [==============================] - 1s 405us/sample - loss: 0.0830 - accuracy: 0.9725 - val_loss: 0.1263 - val_accuracy: 0.9632
+Epoch 19/30
+
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2327/2327 [==============================] - 1s 397us/sample - loss: 0.0688 - accuracy: 0.9850 - val_loss: 0.1243 - val_accuracy: 0.9644
+Epoch 20/30
+
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2327/2327 [==============================] - 1s 386us/sample - loss: 0.0705 - accuracy: 0.9832 - val_loss: 0.1219 - val_accuracy: 0.9720
+Epoch 21/30
+
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2327/2327 [==============================] - 1s 387us/sample - loss: 0.0596 - accuracy: 0.9854 - val_loss: 0.1217 - val_accuracy: 0.9670
+Epoch 22/30
+
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2327/2327 [==============================] - 1s 387us/sample - loss: 0.0628 - accuracy: 0.9841 - val_loss: 0.1170 - val_accuracy: 0.9657
+Epoch 23/30
+
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2327/2327 [==============================] - 1s 389us/sample - loss: 0.0553 - accuracy: 0.9884 - val_loss: 0.1052 - val_accuracy: 0.9695
+Epoch 24/30
+
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2327/2327 [==============================] - 1s 386us/sample - loss: 0.0566 - accuracy: 0.9841 - val_loss: 0.1034 - val_accuracy: 0.9733
+Epoch 25/30
+
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2327/2327 [==============================] - 1s 383us/sample - loss: 0.0507 - accuracy: 0.9897 - val_loss: 0.1035 - val_accuracy: 0.9720
+Epoch 26/30
+
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2327/2327 [==============================] - 1s 388us/sample - loss: 0.0431 - accuracy: 0.9923 - val_loss: 0.0985 - val_accuracy: 0.9682
+Epoch 27/30
+
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2176/2327 [===========================>..] - ETA: 0s - loss: 0.0450 - accuracy: 0.9908
2327/2327 [==============================] - 1s 384us/sample - loss: 0.0463 - accuracy: 0.9901 - val_loss: 0.1041 - val_accuracy: 0.9720
+Epoch 28/30
+
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2327/2327 [==============================] - 1s 386us/sample - loss: 0.0351 - accuracy: 0.9931 - val_loss: 0.0895 - val_accuracy: 0.9746
+Epoch 29/30
+
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2176/2327 [===========================>..] - ETA: 0s - loss: 0.0397 - accuracy: 0.9894
2327/2327 [==============================] - 1s 386us/sample - loss: 0.0386 - accuracy: 0.9901 - val_loss: 0.0903 - val_accuracy: 0.9733
+Epoch 30/30
+
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2176/2327 [===========================>..] - ETA: 0s - loss: 0.0337 - accuracy: 0.9922
2327/2327 [==============================] - 1s 389us/sample - loss: 0.0332 - accuracy: 0.9927 - val_loss: 0.0844 - val_accuracy: 0.9746
+Train on 699 samples, validate on 2107 samples
+Epoch 1/30
+
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256/699 [=========>....................] - ETA: 0s - loss: 2.8495 - accuracy: 0.1875
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640/699 [==========================>...] - ETA: 0s - loss: 2.5944 - accuracy: 0.2016
699/699 [==============================] - 1s 2ms/sample - loss: 2.5723 - accuracy: 0.2003 - val_loss: 1.6085 - val_accuracy: 0.3166
+Epoch 2/30
+
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256/699 [=========>....................] - ETA: 0s - loss: 1.8685 - accuracy: 0.3047
448/699 [==================>...........] - ETA: 0s - loss: 1.8052 - accuracy: 0.3259
640/699 [==========================>...] - ETA: 0s - loss: 1.7294 - accuracy: 0.3469
699/699 [==============================] - 1s 861us/sample - loss: 1.7104 - accuracy: 0.3462 - val_loss: 1.2380 - val_accuracy: 0.5026
+Epoch 3/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 1.2503 - accuracy: 0.5406
699/699 [==============================] - 1s 867us/sample - loss: 1.2339 - accuracy: 0.5422 - val_loss: 1.0640 - val_accuracy: 0.5880
+Epoch 4/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 1.0042 - accuracy: 0.6031
699/699 [==============================] - 1s 859us/sample - loss: 0.9864 - accuracy: 0.6123 - val_loss: 0.9182 - val_accuracy: 0.6768
+Epoch 5/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.8254 - accuracy: 0.7094
699/699 [==============================] - 1s 856us/sample - loss: 0.8270 - accuracy: 0.7096 - val_loss: 0.8285 - val_accuracy: 0.7262
+Epoch 6/30
+
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699/699 [==============================] - 1s 851us/sample - loss: 0.6924 - accuracy: 0.7711 - val_loss: 0.7429 - val_accuracy: 0.7665
+Epoch 7/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.6299 - accuracy: 0.7875
699/699 [==============================] - 1s 855us/sample - loss: 0.6274 - accuracy: 0.7868 - val_loss: 0.6689 - val_accuracy: 0.7969
+Epoch 8/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.5065 - accuracy: 0.8516
699/699 [==============================] - 1s 851us/sample - loss: 0.5073 - accuracy: 0.8526 - val_loss: 0.6255 - val_accuracy: 0.8092
+Epoch 9/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.4573 - accuracy: 0.8781
699/699 [==============================] - 1s 855us/sample - loss: 0.4554 - accuracy: 0.8784 - val_loss: 0.5394 - val_accuracy: 0.8519
+Epoch 10/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.4094 - accuracy: 0.8719
699/699 [==============================] - 1s 851us/sample - loss: 0.4162 - accuracy: 0.8684 - val_loss: 0.5308 - val_accuracy: 0.8462
+Epoch 11/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.3505 - accuracy: 0.9078
699/699 [==============================] - 1s 857us/sample - loss: 0.3541 - accuracy: 0.9027 - val_loss: 0.4858 - val_accuracy: 0.8614
+Epoch 12/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.3539 - accuracy: 0.8984
699/699 [==============================] - 1s 860us/sample - loss: 0.3540 - accuracy: 0.8970 - val_loss: 0.4562 - val_accuracy: 0.8685
+Epoch 13/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.3263 - accuracy: 0.9047
699/699 [==============================] - 1s 853us/sample - loss: 0.3326 - accuracy: 0.9041 - val_loss: 0.4264 - val_accuracy: 0.8780
+Epoch 14/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.2889 - accuracy: 0.9109
699/699 [==============================] - 1s 855us/sample - loss: 0.2822 - accuracy: 0.9142 - val_loss: 0.4049 - val_accuracy: 0.8776
+Epoch 15/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.2472 - accuracy: 0.9266
699/699 [==============================] - 1s 849us/sample - loss: 0.2376 - accuracy: 0.9313 - val_loss: 0.3726 - val_accuracy: 0.8904
+Epoch 16/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.2808 - accuracy: 0.9219
699/699 [==============================] - 1s 857us/sample - loss: 0.2751 - accuracy: 0.9242 - val_loss: 0.3673 - val_accuracy: 0.8894
+Epoch 17/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.2118 - accuracy: 0.9453
699/699 [==============================] - 1s 852us/sample - loss: 0.2121 - accuracy: 0.9428 - val_loss: 0.3527 - val_accuracy: 0.8965
+Epoch 18/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.1838 - accuracy: 0.9547
699/699 [==============================] - 1s 852us/sample - loss: 0.1856 - accuracy: 0.9528 - val_loss: 0.3299 - val_accuracy: 0.9003
+Epoch 19/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.2088 - accuracy: 0.9438
699/699 [==============================] - 1s 855us/sample - loss: 0.1960 - accuracy: 0.9471 - val_loss: 0.3444 - val_accuracy: 0.8913
+Epoch 20/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.1744 - accuracy: 0.9578
699/699 [==============================] - 1s 859us/sample - loss: 0.1769 - accuracy: 0.9571 - val_loss: 0.3120 - val_accuracy: 0.9084
+Epoch 21/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.1730 - accuracy: 0.9609
699/699 [==============================] - 1s 853us/sample - loss: 0.1700 - accuracy: 0.9628 - val_loss: 0.3099 - val_accuracy: 0.9056
+Epoch 22/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.1631 - accuracy: 0.9594
699/699 [==============================] - 1s 855us/sample - loss: 0.1617 - accuracy: 0.9599 - val_loss: 0.2889 - val_accuracy: 0.9160
+Epoch 23/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.1408 - accuracy: 0.9703
699/699 [==============================] - 1s 851us/sample - loss: 0.1392 - accuracy: 0.9714 - val_loss: 0.2782 - val_accuracy: 0.9169
+Epoch 24/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.1220 - accuracy: 0.9812
699/699 [==============================] - 1s 847us/sample - loss: 0.1185 - accuracy: 0.9828 - val_loss: 0.2805 - val_accuracy: 0.9117
+Epoch 25/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.1410 - accuracy: 0.9547
699/699 [==============================] - 1s 847us/sample - loss: 0.1395 - accuracy: 0.9571 - val_loss: 0.2553 - val_accuracy: 0.9226
+Epoch 26/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.1125 - accuracy: 0.9719
699/699 [==============================] - 1s 855us/sample - loss: 0.1125 - accuracy: 0.9728 - val_loss: 0.2590 - val_accuracy: 0.9198
+Epoch 27/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.1126 - accuracy: 0.9766
699/699 [==============================] - 1s 857us/sample - loss: 0.1147 - accuracy: 0.9742 - val_loss: 0.2514 - val_accuracy: 0.9236
+Epoch 28/30
+
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640/699 [==========================>...] - ETA: 0s - loss: 0.1136 - accuracy: 0.9812
699/699 [==============================] - 1s 854us/sample - loss: 0.1101 - accuracy: 0.9828 - val_loss: 0.2489 - val_accuracy: 0.9198
+Epoch 29/30
+
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256/699 [=========>....................] - ETA: 0s - loss: 0.0896 - accuracy: 0.9883
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640/699 [==========================>...] - ETA: 0s - loss: 0.1087 - accuracy: 0.9734
699/699 [==============================] - 1s 853us/sample - loss: 0.1040 - accuracy: 0.9757 - val_loss: 0.2504 - val_accuracy: 0.9179
+Epoch 30/30
+
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256/699 [=========>....................] - ETA: 0s - loss: 0.0889 - accuracy: 0.9844
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640/699 [==========================>...] - ETA: 0s - loss: 0.0940 - accuracy: 0.9797
699/699 [==============================] - 1s 848us/sample - loss: 0.0914 - accuracy: 0.9814 - val_loss: 0.2403 - val_accuracy: 0.9269
+Train on 787 samples, validate on 2327 samples
+Epoch 1/30
+
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256/787 [========>.....................] - ETA: 0s - loss: 3.5834 - accuracy: 0.1875
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640/787 [=======================>......] - ETA: 0s - loss: 3.1233 - accuracy: 0.2219
787/787 [==============================] - 1s 2ms/sample - loss: 3.0042 - accuracy: 0.2313 - val_loss: 1.9668 - val_accuracy: 0.2454
+Epoch 2/30
+
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448/787 [================>.............] - ETA: 0s - loss: 2.1120 - accuracy: 0.2857
640/787 [=======================>......] - ETA: 0s - loss: 2.0137 - accuracy: 0.3125
787/787 [==============================] - 1s 886us/sample - loss: 1.9649 - accuracy: 0.3291 - val_loss: 1.5912 - val_accuracy: 0.3537
+Epoch 3/30
+
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787/787 [==============================] - 1s 873us/sample - loss: 1.4261 - accuracy: 0.4689 - val_loss: 1.3249 - val_accuracy: 0.4817
+Epoch 4/30
+
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787/787 [==============================] - 1s 864us/sample - loss: 1.1367 - accuracy: 0.5591 - val_loss: 1.1542 - val_accuracy: 0.5531
+Epoch 5/30
+
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787/787 [==============================] - 1s 865us/sample - loss: 0.9301 - accuracy: 0.6429 - val_loss: 1.0795 - val_accuracy: 0.5978
+Epoch 6/30
+
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787/787 [==============================] - 1s 847us/sample - loss: 0.7988 - accuracy: 0.6976 - val_loss: 0.9825 - val_accuracy: 0.6446
+Epoch 7/30
+
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787/787 [==============================] - 1s 851us/sample - loss: 0.6965 - accuracy: 0.7548 - val_loss: 0.8719 - val_accuracy: 0.7086
+Epoch 8/30
+
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787/787 [==============================] - 1s 852us/sample - loss: 0.5871 - accuracy: 0.8170 - val_loss: 0.8260 - val_accuracy: 0.7134
+Epoch 9/30
+
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787/787 [==============================] - 1s 842us/sample - loss: 0.5282 - accuracy: 0.8234 - val_loss: 0.7554 - val_accuracy: 0.7473
+Epoch 10/30
+
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787/787 [==============================] - 1s 849us/sample - loss: 0.4756 - accuracy: 0.8463 - val_loss: 0.7256 - val_accuracy: 0.7460
+Epoch 11/30
+
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787/787 [==============================] - 1s 841us/sample - loss: 0.4352 - accuracy: 0.8590 - val_loss: 0.6980 - val_accuracy: 0.7572
+Epoch 12/30
+
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787/787 [==============================] - 1s 845us/sample - loss: 0.3972 - accuracy: 0.8704 - val_loss: 0.6312 - val_accuracy: 0.7903
+Epoch 13/30
+
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787/787 [==============================] - 1s 845us/sample - loss: 0.3583 - accuracy: 0.8983 - val_loss: 0.6322 - val_accuracy: 0.7765
+Epoch 14/30
+
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787/787 [==============================] - 1s 846us/sample - loss: 0.3055 - accuracy: 0.9060 - val_loss: 0.6162 - val_accuracy: 0.7843
+Epoch 15/30
+
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787/787 [==============================] - 1s 843us/sample - loss: 0.2938 - accuracy: 0.9199 - val_loss: 0.5501 - val_accuracy: 0.8169
+Epoch 16/30
+
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787/787 [==============================] - 1s 854us/sample - loss: 0.3034 - accuracy: 0.9022 - val_loss: 0.5691 - val_accuracy: 0.8006
+Epoch 17/30
+
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787/787 [==============================] - 1s 846us/sample - loss: 0.2410 - accuracy: 0.9403 - val_loss: 0.5606 - val_accuracy: 0.8040
+Epoch 18/30
+
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787/787 [==============================] - 1s 844us/sample - loss: 0.2246 - accuracy: 0.9390 - val_loss: 0.5007 - val_accuracy: 0.8315
+Epoch 19/30
+
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787/787 [==============================] - 1s 845us/sample - loss: 0.2152 - accuracy: 0.9492 - val_loss: 0.5058 - val_accuracy: 0.8247
+Epoch 20/30
+
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787/787 [==============================] - 1s 842us/sample - loss: 0.1888 - accuracy: 0.9479 - val_loss: 0.5296 - val_accuracy: 0.8174
+Epoch 21/30
+
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787/787 [==============================] - 1s 837us/sample - loss: 0.1940 - accuracy: 0.9428 - val_loss: 0.4620 - val_accuracy: 0.8427
+Epoch 22/30
+
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787/787 [==============================] - 1s 841us/sample - loss: 0.1717 - accuracy: 0.9568 - val_loss: 0.4715 - val_accuracy: 0.8397
+Epoch 23/30
+
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787/787 [==============================] - 1s 847us/sample - loss: 0.1631 - accuracy: 0.9555 - val_loss: 0.4751 - val_accuracy: 0.8350
+Epoch 24/30
+
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787/787 [==============================] - 1s 862us/sample - loss: 0.1454 - accuracy: 0.9708 - val_loss: 0.4455 - val_accuracy: 0.8500
+Epoch 25/30
+
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787/787 [==============================] - 1s 852us/sample - loss: 0.1436 - accuracy: 0.9720 - val_loss: 0.4408 - val_accuracy: 0.8483
+Epoch 26/30
+
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787/787 [==============================] - 1s 850us/sample - loss: 0.1106 - accuracy: 0.9835 - val_loss: 0.4320 - val_accuracy: 0.8500
+Epoch 27/30
+
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787/787 [==============================] - 1s 840us/sample - loss: 0.1127 - accuracy: 0.9822 - val_loss: 0.4212 - val_accuracy: 0.8556
+Epoch 28/30
+
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787/787 [==============================] - 1s 847us/sample - loss: 0.1216 - accuracy: 0.9682 - val_loss: 0.4185 - val_accuracy: 0.8547
+Epoch 29/30
+
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787/787 [==============================] - 1s 840us/sample - loss: 0.1111 - accuracy: 0.9784 - val_loss: 0.4358 - val_accuracy: 0.8526
+Epoch 30/30
+
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787/787 [==============================] - 1s 848us/sample - loss: 0.1010 - accuracy: 0.9835 - val_loss: 0.4012 - val_accuracy: 0.8625
+Train on 2091 samples, validate on 715 samples
+Epoch 1/30
+
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2091/2091 [==============================] - 1s 674us/sample - loss: 2.6588 - accuracy: 0.2802 - val_loss: 0.9940 - val_accuracy: 0.6154
+Epoch 2/30
+
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2091/2091 [==============================] - 1s 396us/sample - loss: 1.0379 - accuracy: 0.6126 - val_loss: 0.5521 - val_accuracy: 0.8308
+Epoch 3/30
+
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2091/2091 [==============================] - 1s 395us/sample - loss: 0.6083 - accuracy: 0.7814 - val_loss: 0.4412 - val_accuracy: 0.8699
+Epoch 4/30
+
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2091/2091 [==============================] - 1s 400us/sample - loss: 0.4738 - accuracy: 0.8407 - val_loss: 0.3354 - val_accuracy: 0.9021
+Epoch 5/30
+
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2091/2091 [==============================] - 1s 402us/sample - loss: 0.3507 - accuracy: 0.8905 - val_loss: 0.3022 - val_accuracy: 0.9147
+Epoch 6/30
+
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2091/2091 [==============================] - 1s 395us/sample - loss: 0.2806 - accuracy: 0.9149 - val_loss: 0.2321 - val_accuracy: 0.9371
+Epoch 7/30
+
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2091/2091 [==============================] - 1s 390us/sample - loss: 0.2490 - accuracy: 0.9173 - val_loss: 0.2163 - val_accuracy: 0.9413
+Epoch 8/30
+
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2091/2091 [==============================] - 1s 394us/sample - loss: 0.2177 - accuracy: 0.9321 - val_loss: 0.2104 - val_accuracy: 0.9343
+Epoch 9/30
+
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2091/2091 [==============================] - 1s 388us/sample - loss: 0.1925 - accuracy: 0.9450 - val_loss: 0.1742 - val_accuracy: 0.9497
+Epoch 10/30
+
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2091/2091 [==============================] - 1s 385us/sample - loss: 0.1746 - accuracy: 0.9493 - val_loss: 0.1591 - val_accuracy: 0.9510
+Epoch 11/30
+
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2091/2091 [==============================] - 1s 389us/sample - loss: 0.1351 - accuracy: 0.9627 - val_loss: 0.1602 - val_accuracy: 0.9483
+Epoch 12/30
+
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2091/2091 [==============================] - 1s 393us/sample - loss: 0.1283 - accuracy: 0.9622 - val_loss: 0.1462 - val_accuracy: 0.9566
+Epoch 13/30
+
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2091/2091 [==============================] - 1s 390us/sample - loss: 0.1196 - accuracy: 0.9627 - val_loss: 0.1486 - val_accuracy: 0.9469
+Epoch 14/30
+
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2091/2091 [==============================] - 1s 391us/sample - loss: 0.1132 - accuracy: 0.9694 - val_loss: 0.1377 - val_accuracy: 0.9538
+Epoch 15/30
+
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2091/2091 [==============================] - 1s 386us/sample - loss: 0.0978 - accuracy: 0.9756 - val_loss: 0.1305 - val_accuracy: 0.9580
+Epoch 16/30
+
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2091/2091 [==============================] - 1s 390us/sample - loss: 0.0972 - accuracy: 0.9675 - val_loss: 0.1142 - val_accuracy: 0.9664
+Epoch 17/30
+
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2091/2091 [==============================] - 1s 391us/sample - loss: 0.0812 - accuracy: 0.9809 - val_loss: 0.1206 - val_accuracy: 0.9622
+Epoch 18/30
+
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1984/2091 [===========================>..] - ETA: 0s - loss: 0.0813 - accuracy: 0.9748
2091/2091 [==============================] - 1s 391us/sample - loss: 0.0828 - accuracy: 0.9737 - val_loss: 0.1363 - val_accuracy: 0.9552
+Epoch 19/30
+
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1984/2091 [===========================>..] - ETA: 0s - loss: 0.0756 - accuracy: 0.9768
2091/2091 [==============================] - 1s 389us/sample - loss: 0.0758 - accuracy: 0.9766 - val_loss: 0.1109 - val_accuracy: 0.9622
+Epoch 20/30
+
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1984/2091 [===========================>..] - ETA: 0s - loss: 0.0625 - accuracy: 0.9844
2091/2091 [==============================] - 1s 389us/sample - loss: 0.0634 - accuracy: 0.9837 - val_loss: 0.1185 - val_accuracy: 0.9608
+Epoch 21/30
+
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1984/2091 [===========================>..] - ETA: 0s - loss: 0.0628 - accuracy: 0.9829
2091/2091 [==============================] - 1s 387us/sample - loss: 0.0643 - accuracy: 0.9813 - val_loss: 0.1057 - val_accuracy: 0.9678
+Epoch 22/30
+
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1984/2091 [===========================>..] - ETA: 0s - loss: 0.0559 - accuracy: 0.9879
2091/2091 [==============================] - 1s 387us/sample - loss: 0.0557 - accuracy: 0.9880 - val_loss: 0.1067 - val_accuracy: 0.9636
+Epoch 23/30
+
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2091/2091 [==============================] - 1s 388us/sample - loss: 0.0586 - accuracy: 0.9861 - val_loss: 0.1026 - val_accuracy: 0.9622
+Epoch 24/30
+
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2091/2091 [==============================] - 1s 388us/sample - loss: 0.0520 - accuracy: 0.9885 - val_loss: 0.1071 - val_accuracy: 0.9664
+Epoch 25/30
+
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2091/2091 [==============================] - 1s 391us/sample - loss: 0.0531 - accuracy: 0.9833 - val_loss: 0.1097 - val_accuracy: 0.9622
+Epoch 26/30
+
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2091/2091 [==============================] - 1s 388us/sample - loss: 0.0463 - accuracy: 0.9885 - val_loss: 0.0938 - val_accuracy: 0.9636
+Epoch 27/30
+
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2091/2091 [==============================] - 1s 390us/sample - loss: 0.0406 - accuracy: 0.9890 - val_loss: 0.0976 - val_accuracy: 0.9678
+Epoch 28/30
+
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2091/2091 [==============================] - 1s 391us/sample - loss: 0.0430 - accuracy: 0.9895 - val_loss: 0.1055 - val_accuracy: 0.9678
+Epoch 29/30
+
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2091/2091 [==============================] - 1s 390us/sample - loss: 0.0394 - accuracy: 0.9900 - val_loss: 0.0961 - val_accuracy: 0.9650
+Epoch 30/30
+
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2091/2091 [==============================] - 1s 388us/sample - loss: 0.0396 - accuracy: 0.9909 - val_loss: 0.1043 - val_accuracy: 0.9594
+Train on 2275 samples, validate on 839 samples
+Epoch 1/30
+
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2275/2275 [==============================] - 1s 619us/sample - loss: 2.1104 - accuracy: 0.3530 - val_loss: 1.3633 - val_accuracy: 0.4315
+Epoch 2/30
+
  64/2275 [..............................] - ETA: 0s - loss: 1.3870 - accuracy: 0.4688
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2275/2275 [==============================] - 1s 395us/sample - loss: 1.0061 - accuracy: 0.6127 - val_loss: 0.8622 - val_accuracy: 0.6889
+Epoch 3/30
+
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2275/2275 [==============================] - 1s 392us/sample - loss: 0.6587 - accuracy: 0.7618 - val_loss: 0.7355 - val_accuracy: 0.7604
+Epoch 4/30
+
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2275/2275 [==============================] - 1s 394us/sample - loss: 0.4858 - accuracy: 0.8369 - val_loss: 0.5632 - val_accuracy: 0.8379
+Epoch 5/30
+
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2275/2275 [==============================] - 1s 392us/sample - loss: 0.3991 - accuracy: 0.8712 - val_loss: 0.5053 - val_accuracy: 0.8546
+Epoch 6/30
+
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 256/2275 [==>...........................] - ETA: 0s - loss: 0.3600 - accuracy: 0.8594
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2275/2275 [==============================] - 1s 389us/sample - loss: 0.3289 - accuracy: 0.8884 - val_loss: 0.4434 - val_accuracy: 0.8617
+Epoch 7/30
+
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 256/2275 [==>...........................] - ETA: 0s - loss: 0.2768 - accuracy: 0.9102
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2275/2275 [==============================] - 1s 391us/sample - loss: 0.2637 - accuracy: 0.9134 - val_loss: 0.3770 - val_accuracy: 0.8701
+Epoch 8/30
+
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 256/2275 [==>...........................] - ETA: 0s - loss: 0.2265 - accuracy: 0.9414
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1024/2275 [============>.................] - ETA: 0s - loss: 0.2259 - accuracy: 0.9404
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2275/2275 [==============================] - 1s 392us/sample - loss: 0.2261 - accuracy: 0.9310 - val_loss: 0.3564 - val_accuracy: 0.8927
+Epoch 9/30
+
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2275/2275 [==============================] - 1s 389us/sample - loss: 0.1904 - accuracy: 0.9433 - val_loss: 0.3573 - val_accuracy: 0.8939
+Epoch 10/30
+
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2275/2275 [==============================] - 1s 398us/sample - loss: 0.1659 - accuracy: 0.9525 - val_loss: 0.2979 - val_accuracy: 0.9130
+Epoch 11/30
+
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2275/2275 [==============================] - 1s 387us/sample - loss: 0.1479 - accuracy: 0.9600 - val_loss: 0.2847 - val_accuracy: 0.9106
+Epoch 12/30
+
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2275/2275 [==============================] - 1s 390us/sample - loss: 0.1296 - accuracy: 0.9618 - val_loss: 0.2649 - val_accuracy: 0.9142
+Epoch 13/30
+
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2275/2275 [==============================] - 1s 390us/sample - loss: 0.1196 - accuracy: 0.9670 - val_loss: 0.2471 - val_accuracy: 0.9261
+Epoch 14/30
+
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2275/2275 [==============================] - 1s 389us/sample - loss: 0.1133 - accuracy: 0.9662 - val_loss: 0.2729 - val_accuracy: 0.9130
+Epoch 15/30
+
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2275/2275 [==============================] - 1s 388us/sample - loss: 0.0914 - accuracy: 0.9767 - val_loss: 0.2333 - val_accuracy: 0.9225
+Epoch 16/30
+
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2275/2275 [==============================] - 1s 390us/sample - loss: 0.0919 - accuracy: 0.9745 - val_loss: 0.2608 - val_accuracy: 0.9082
+Epoch 17/30
+
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2275/2275 [==============================] - 1s 389us/sample - loss: 0.0814 - accuracy: 0.9811 - val_loss: 0.2451 - val_accuracy: 0.9190
+Epoch 18/30
+
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2275/2275 [==============================] - 1s 390us/sample - loss: 0.0728 - accuracy: 0.9824 - val_loss: 0.2299 - val_accuracy: 0.9213
+Epoch 19/30
+
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2275/2275 [==============================] - 1s 393us/sample - loss: 0.0670 - accuracy: 0.9855 - val_loss: 0.1973 - val_accuracy: 0.9321
+Epoch 20/30
+
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 256/2275 [==>...........................] - ETA: 0s - loss: 0.0582 - accuracy: 0.9922
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2275/2275 [==============================] - 1s 390us/sample - loss: 0.0628 - accuracy: 0.9873 - val_loss: 0.2068 - val_accuracy: 0.9309
+Epoch 21/30
+
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 256/2275 [==>...........................] - ETA: 0s - loss: 0.0773 - accuracy: 0.9844
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2275/2275 [==============================] - 1s 387us/sample - loss: 0.0614 - accuracy: 0.9846 - val_loss: 0.2077 - val_accuracy: 0.9249
+Epoch 22/30
+
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2275/2275 [==============================] - 1s 391us/sample - loss: 0.0517 - accuracy: 0.9912 - val_loss: 0.2069 - val_accuracy: 0.9344
+Epoch 23/30
+
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 256/2275 [==>...........................] - ETA: 0s - loss: 0.0328 - accuracy: 1.0000
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2275/2275 [==============================] - 1s 391us/sample - loss: 0.0487 - accuracy: 0.9899 - val_loss: 0.1903 - val_accuracy: 0.9356
+Epoch 24/30
+
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2275/2275 [==============================] - 1s 390us/sample - loss: 0.0426 - accuracy: 0.9916 - val_loss: 0.1909 - val_accuracy: 0.9333
+Epoch 25/30
+
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 256/2275 [==>...........................] - ETA: 0s - loss: 0.0375 - accuracy: 0.9961
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2275/2275 [==============================] - 1s 386us/sample - loss: 0.0443 - accuracy: 0.9895 - val_loss: 0.2036 - val_accuracy: 0.9285
+Epoch 26/30
+
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2275/2275 [==============================] - 1s 387us/sample - loss: 0.0422 - accuracy: 0.9912 - val_loss: 0.1727 - val_accuracy: 0.9452
+Epoch 27/30
+
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 256/2275 [==>...........................] - ETA: 0s - loss: 0.0381 - accuracy: 1.0000
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2275/2275 [==============================] - 1s 387us/sample - loss: 0.0400 - accuracy: 0.9899 - val_loss: 0.1941 - val_accuracy: 0.9321
+Epoch 28/30
+
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2275/2275 [==============================] - 1s 391us/sample - loss: 0.0337 - accuracy: 0.9908 - val_loss: 0.1836 - val_accuracy: 0.9344
+Epoch 29/30
+
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2275/2275 [==============================] - 1s 388us/sample - loss: 0.0345 - accuracy: 0.9934 - val_loss: 0.1927 - val_accuracy: 0.9356
+Epoch 30/30
+
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2275/2275 [==============================] - 1s 389us/sample - loss: 0.0302 - accuracy: 0.9952 - val_loss: 0.1897 - val_accuracy: 0.9380
+Train on 715 samples, validate on 2091 samples
+Epoch 1/30
+
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715/715 [==============================] - 1s 2ms/sample - loss: 3.0052 - accuracy: 0.2755 - val_loss: 1.5972 - val_accuracy: 0.3721
+Epoch 2/30
+
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715/715 [==============================] - 1s 845us/sample - loss: 1.8490 - accuracy: 0.4140 - val_loss: 1.0722 - val_accuracy: 0.5844
+Epoch 3/30
+
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715/715 [==============================] - 1s 849us/sample - loss: 1.2175 - accuracy: 0.5706 - val_loss: 0.8693 - val_accuracy: 0.6949
+Epoch 4/30
+
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715/715 [==============================] - 1s 844us/sample - loss: 0.8841 - accuracy: 0.6545 - val_loss: 0.7381 - val_accuracy: 0.7389
+Epoch 5/30
+
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715/715 [==============================] - 1s 849us/sample - loss: 0.7599 - accuracy: 0.7217 - val_loss: 0.6306 - val_accuracy: 0.7934
+Epoch 6/30
+
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715/715 [==============================] - 1s 846us/sample - loss: 0.5069 - accuracy: 0.8238 - val_loss: 0.5529 - val_accuracy: 0.8250
+Epoch 7/30
+
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715/715 [==============================] - 1s 844us/sample - loss: 0.4601 - accuracy: 0.8476 - val_loss: 0.5196 - val_accuracy: 0.8345
+Epoch 8/30
+
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715/715 [==============================] - 1s 847us/sample - loss: 0.4357 - accuracy: 0.8559 - val_loss: 0.4691 - val_accuracy: 0.8604
+Epoch 9/30
+
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715/715 [==============================] - 1s 842us/sample - loss: 0.3485 - accuracy: 0.8853 - val_loss: 0.4392 - val_accuracy: 0.8661
+Epoch 10/30
+
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715/715 [==============================] - 1s 840us/sample - loss: 0.3495 - accuracy: 0.8881 - val_loss: 0.4079 - val_accuracy: 0.8757
+Epoch 11/30
+
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715/715 [==============================] - 1s 839us/sample - loss: 0.2905 - accuracy: 0.9077 - val_loss: 0.3875 - val_accuracy: 0.8900
+Epoch 12/30
+
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715/715 [==============================] - 1s 844us/sample - loss: 0.2561 - accuracy: 0.9385 - val_loss: 0.3673 - val_accuracy: 0.8934
+Epoch 13/30
+
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715/715 [==============================] - 1s 840us/sample - loss: 0.2277 - accuracy: 0.9301 - val_loss: 0.3554 - val_accuracy: 0.8953
+Epoch 14/30
+
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715/715 [==============================] - 1s 846us/sample - loss: 0.2039 - accuracy: 0.9483 - val_loss: 0.3391 - val_accuracy: 0.9015
+Epoch 15/30
+
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715/715 [==============================] - 1s 844us/sample - loss: 0.2051 - accuracy: 0.9413 - val_loss: 0.3295 - val_accuracy: 0.9034
+Epoch 16/30
+
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715/715 [==============================] - 1s 846us/sample - loss: 0.1794 - accuracy: 0.9538 - val_loss: 0.2963 - val_accuracy: 0.9216
+Epoch 17/30
+
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715/715 [==============================] - 1s 845us/sample - loss: 0.1576 - accuracy: 0.9580 - val_loss: 0.3072 - val_accuracy: 0.9106
+Epoch 18/30
+
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715/715 [==============================] - 1s 840us/sample - loss: 0.1426 - accuracy: 0.9678 - val_loss: 0.2928 - val_accuracy: 0.9120
+Epoch 19/30
+
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715/715 [==============================] - 1s 844us/sample - loss: 0.1256 - accuracy: 0.9678 - val_loss: 0.2760 - val_accuracy: 0.9206
+Epoch 20/30
+
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715/715 [==============================] - 1s 838us/sample - loss: 0.1200 - accuracy: 0.9734 - val_loss: 0.2747 - val_accuracy: 0.9211
+Epoch 21/30
+
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715/715 [==============================] - 1s 845us/sample - loss: 0.1283 - accuracy: 0.9678 - val_loss: 0.2715 - val_accuracy: 0.9201
+Epoch 22/30
+
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715/715 [==============================] - 1s 846us/sample - loss: 0.1123 - accuracy: 0.9776 - val_loss: 0.2531 - val_accuracy: 0.9292
+Epoch 23/30
+
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640/715 [=========================>....] - ETA: 0s - loss: 0.1124 - accuracy: 0.9797
715/715 [==============================] - 1s 843us/sample - loss: 0.1086 - accuracy: 0.9818 - val_loss: 0.2463 - val_accuracy: 0.9326
+Epoch 24/30
+
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715/715 [==============================] - 1s 842us/sample - loss: 0.0946 - accuracy: 0.9748 - val_loss: 0.2391 - val_accuracy: 0.9321
+Epoch 25/30
+
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715/715 [==============================] - 1s 842us/sample - loss: 0.1045 - accuracy: 0.9734 - val_loss: 0.2638 - val_accuracy: 0.9220
+Epoch 26/30
+
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715/715 [==============================] - 1s 847us/sample - loss: 0.0789 - accuracy: 0.9832 - val_loss: 0.2359 - val_accuracy: 0.9326
+Epoch 27/30
+
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715/715 [==============================] - 1s 850us/sample - loss: 0.0913 - accuracy: 0.9776 - val_loss: 0.2353 - val_accuracy: 0.9311
+Epoch 28/30
+
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715/715 [==============================] - 1s 840us/sample - loss: 0.0758 - accuracy: 0.9832 - val_loss: 0.2402 - val_accuracy: 0.9273
+Epoch 29/30
+
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715/715 [==============================] - 1s 851us/sample - loss: 0.0755 - accuracy: 0.9832 - val_loss: 0.2174 - val_accuracy: 0.9407
+Epoch 30/30
+
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715/715 [==============================] - 1s 841us/sample - loss: 0.0876 - accuracy: 0.9748 - val_loss: 0.2367 - val_accuracy: 0.9287
+Train on 839 samples, validate on 2275 samples
+Epoch 1/30
+
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256/839 [========>.....................] - ETA: 1s - loss: 3.0435 - accuracy: 0.2266
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839/839 [==============================] - 1s 1ms/sample - loss: 2.5097 - accuracy: 0.2956 - val_loss: 1.5031 - val_accuracy: 0.3868
+Epoch 2/30
+
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839/839 [==============================] - 1s 814us/sample - loss: 1.6228 - accuracy: 0.4207 - val_loss: 1.2658 - val_accuracy: 0.4725
+Epoch 3/30
+
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839/839 [==============================] - 1s 807us/sample - loss: 1.1319 - accuracy: 0.5721 - val_loss: 1.1601 - val_accuracy: 0.5429
+Epoch 4/30
+
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839/839 [==============================] - 1s 829us/sample - loss: 0.9103 - accuracy: 0.6651 - val_loss: 1.0643 - val_accuracy: 0.5609
+Epoch 5/30
+
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839/839 [==============================] - 1s 803us/sample - loss: 0.7522 - accuracy: 0.7247 - val_loss: 0.9993 - val_accuracy: 0.6136
+Epoch 6/30
+
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839/839 [==============================] - 1s 816us/sample - loss: 0.6691 - accuracy: 0.7569 - val_loss: 0.9495 - val_accuracy: 0.6215
+Epoch 7/30
+
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839/839 [==============================] - 1s 812us/sample - loss: 0.5865 - accuracy: 0.7962 - val_loss: 0.9484 - val_accuracy: 0.6255
+Epoch 8/30
+
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839/839 [==============================] - 1s 812us/sample - loss: 0.4771 - accuracy: 0.8367 - val_loss: 0.8415 - val_accuracy: 0.6796
+Epoch 9/30
+
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839/839 [==============================] - 1s 806us/sample - loss: 0.4476 - accuracy: 0.8474 - val_loss: 0.8746 - val_accuracy: 0.6673
+Epoch 10/30
+
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839/839 [==============================] - 1s 819us/sample - loss: 0.3924 - accuracy: 0.8534 - val_loss: 0.7508 - val_accuracy: 0.7174
+Epoch 11/30
+
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839/839 [==============================] - 1s 797us/sample - loss: 0.3671 - accuracy: 0.8963 - val_loss: 0.7791 - val_accuracy: 0.7059
+Epoch 12/30
+
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839/839 [==============================] - 1s 806us/sample - loss: 0.3644 - accuracy: 0.8737 - val_loss: 0.7471 - val_accuracy: 0.7257
+Epoch 13/30
+
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839/839 [==============================] - 1s 804us/sample - loss: 0.3204 - accuracy: 0.9023 - val_loss: 0.6980 - val_accuracy: 0.7499
+Epoch 14/30
+
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839/839 [==============================] - 1s 801us/sample - loss: 0.2644 - accuracy: 0.9190 - val_loss: 0.7442 - val_accuracy: 0.7187
+Epoch 15/30
+
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839/839 [==============================] - 1s 801us/sample - loss: 0.2483 - accuracy: 0.9273 - val_loss: 0.6908 - val_accuracy: 0.7490
+Epoch 16/30
+
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839/839 [==============================] - 1s 801us/sample - loss: 0.2190 - accuracy: 0.9428 - val_loss: 0.6577 - val_accuracy: 0.7591
+Epoch 17/30
+
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839/839 [==============================] - 1s 801us/sample - loss: 0.2058 - accuracy: 0.9380 - val_loss: 0.6739 - val_accuracy: 0.7552
+Epoch 18/30
+
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839/839 [==============================] - 1s 806us/sample - loss: 0.1989 - accuracy: 0.9404 - val_loss: 0.6395 - val_accuracy: 0.7675
+Epoch 19/30
+
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839/839 [==============================] - 1s 807us/sample - loss: 0.1679 - accuracy: 0.9559 - val_loss: 0.6673 - val_accuracy: 0.7534
+Epoch 20/30
+
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839/839 [==============================] - 1s 803us/sample - loss: 0.1632 - accuracy: 0.9619 - val_loss: 0.5973 - val_accuracy: 0.7868
+Epoch 21/30
+
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839/839 [==============================] - 1s 806us/sample - loss: 0.1496 - accuracy: 0.9642 - val_loss: 0.6498 - val_accuracy: 0.7657
+Epoch 22/30
+
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839/839 [==============================] - 1s 797us/sample - loss: 0.1587 - accuracy: 0.9631 - val_loss: 0.5821 - val_accuracy: 0.7947
+Epoch 23/30
+
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839/839 [==============================] - 1s 801us/sample - loss: 0.1327 - accuracy: 0.9678 - val_loss: 0.5832 - val_accuracy: 0.7921
+Epoch 24/30
+
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839/839 [==============================] - 1s 797us/sample - loss: 0.1467 - accuracy: 0.9666 - val_loss: 0.6476 - val_accuracy: 0.7666
+Epoch 25/30
+
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839/839 [==============================] - 1s 801us/sample - loss: 0.1226 - accuracy: 0.9702 - val_loss: 0.5582 - val_accuracy: 0.8013
+Epoch 26/30
+
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839/839 [==============================] - 1s 795us/sample - loss: 0.1116 - accuracy: 0.9666 - val_loss: 0.5965 - val_accuracy: 0.7859
+Epoch 27/30
+
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839/839 [==============================] - 1s 799us/sample - loss: 0.0910 - accuracy: 0.9797 - val_loss: 0.5398 - val_accuracy: 0.8110
+Epoch 28/30
+
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839/839 [==============================] - 1s 800us/sample - loss: 0.0878 - accuracy: 0.9809 - val_loss: 0.5821 - val_accuracy: 0.7938
+Epoch 29/30
+
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839/839 [==============================] - 1s 802us/sample - loss: 0.1006 - accuracy: 0.9809 - val_loss: 0.5126 - val_accuracy: 0.8198
+Epoch 30/30
+
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839/839 [==============================] - 1s 797us/sample - loss: 0.0956 - accuracy: 0.9797 - val_loss: 0.6101 - val_accuracy: 0.7829
+Train on 2097 samples, validate on 709 samples
+Epoch 1/30
+
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2097/2097 [==============================] - 1s 675us/sample - loss: 2.3340 - accuracy: 0.3309 - val_loss: 0.9916 - val_accuracy: 0.6305
+Epoch 2/30
+
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2097/2097 [==============================] - 1s 391us/sample - loss: 1.0947 - accuracy: 0.5804 - val_loss: 0.6753 - val_accuracy: 0.7757
+Epoch 3/30
+
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2097/2097 [==============================] - 1s 394us/sample - loss: 0.7353 - accuracy: 0.7234 - val_loss: 0.5398 - val_accuracy: 0.8307
+Epoch 4/30
+
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2097/2097 [==============================] - 1s 389us/sample - loss: 0.5506 - accuracy: 0.8202 - val_loss: 0.4532 - val_accuracy: 0.8561
+Epoch 5/30
+
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2097/2097 [==============================] - 1s 389us/sample - loss: 0.4768 - accuracy: 0.8417 - val_loss: 0.3538 - val_accuracy: 0.9154
+Epoch 6/30
+
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2097/2097 [==============================] - 1s 386us/sample - loss: 0.3870 - accuracy: 0.8717 - val_loss: 0.3105 - val_accuracy: 0.9210
+Epoch 7/30
+
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2097/2097 [==============================] - 1s 393us/sample - loss: 0.3299 - accuracy: 0.9065 - val_loss: 0.2876 - val_accuracy: 0.9238
+Epoch 8/30
+
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2097/2097 [==============================] - 1s 398us/sample - loss: 0.2998 - accuracy: 0.9089 - val_loss: 0.2469 - val_accuracy: 0.9295
+Epoch 9/30
+
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2097/2097 [==============================] - 1s 384us/sample - loss: 0.2499 - accuracy: 0.9251 - val_loss: 0.2250 - val_accuracy: 0.9309
+Epoch 10/30
+
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2097/2097 [==============================] - 1s 388us/sample - loss: 0.2364 - accuracy: 0.9247 - val_loss: 0.2147 - val_accuracy: 0.9422
+Epoch 11/30
+
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2097/2097 [==============================] - 1s 390us/sample - loss: 0.2034 - accuracy: 0.9428 - val_loss: 0.1853 - val_accuracy: 0.9549
+Epoch 12/30
+
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2097/2097 [==============================] - 1s 387us/sample - loss: 0.1834 - accuracy: 0.9485 - val_loss: 0.1888 - val_accuracy: 0.9506
+Epoch 13/30
+
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2097/2097 [==============================] - 1s 391us/sample - loss: 0.1684 - accuracy: 0.9533 - val_loss: 0.1712 - val_accuracy: 0.9520
+Epoch 14/30
+
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2097/2097 [==============================] - 1s 390us/sample - loss: 0.1491 - accuracy: 0.9576 - val_loss: 0.1616 - val_accuracy: 0.9549
+Epoch 15/30
+
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2097/2097 [==============================] - 1s 389us/sample - loss: 0.1408 - accuracy: 0.9619 - val_loss: 0.1485 - val_accuracy: 0.9591
+Epoch 16/30
+
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2097/2097 [==============================] - 1s 423us/sample - loss: 0.1118 - accuracy: 0.9690 - val_loss: 0.1719 - val_accuracy: 0.9436
+Epoch 17/30
+
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2097/2097 [==============================] - 1s 387us/sample - loss: 0.1181 - accuracy: 0.9695 - val_loss: 0.1398 - val_accuracy: 0.9605
+Epoch 18/30
+
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2097/2097 [==============================] - 1s 390us/sample - loss: 0.0950 - accuracy: 0.9757 - val_loss: 0.1408 - val_accuracy: 0.9591
+Epoch 19/30
+
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2097/2097 [==============================] - 1s 386us/sample - loss: 0.1015 - accuracy: 0.9733 - val_loss: 0.1399 - val_accuracy: 0.9633
+Epoch 20/30
+
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2097/2097 [==============================] - 1s 388us/sample - loss: 0.0931 - accuracy: 0.9747 - val_loss: 0.1262 - val_accuracy: 0.9619
+Epoch 21/30
+
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2097/2097 [==============================] - 1s 387us/sample - loss: 0.0914 - accuracy: 0.9733 - val_loss: 0.1340 - val_accuracy: 0.9619
+Epoch 22/30
+
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2097/2097 [==============================] - 1s 387us/sample - loss: 0.0860 - accuracy: 0.9728 - val_loss: 0.1258 - val_accuracy: 0.9619
+Epoch 23/30
+
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2097/2097 [==============================] - 1s 389us/sample - loss: 0.0817 - accuracy: 0.9762 - val_loss: 0.1252 - val_accuracy: 0.9647
+Epoch 24/30
+
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2097/2097 [==============================] - 1s 386us/sample - loss: 0.0665 - accuracy: 0.9824 - val_loss: 0.1129 - val_accuracy: 0.9676
+Epoch 25/30
+
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2097/2097 [==============================] - 1s 402us/sample - loss: 0.0725 - accuracy: 0.9800 - val_loss: 0.1132 - val_accuracy: 0.9661
+Epoch 26/30
+
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2097/2097 [==============================] - 1s 389us/sample - loss: 0.0664 - accuracy: 0.9809 - val_loss: 0.1117 - val_accuracy: 0.9619
+Epoch 27/30
+
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2097/2097 [==============================] - 1s 388us/sample - loss: 0.0647 - accuracy: 0.9843 - val_loss: 0.1296 - val_accuracy: 0.9647
+Epoch 28/30
+
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2097/2097 [==============================] - 1s 385us/sample - loss: 0.0623 - accuracy: 0.9857 - val_loss: 0.1076 - val_accuracy: 0.9647
+Epoch 29/30
+
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2097/2097 [==============================] - 1s 387us/sample - loss: 0.0507 - accuracy: 0.9876 - val_loss: 0.1126 - val_accuracy: 0.9647
+Epoch 30/30
+
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2097/2097 [==============================] - 1s 387us/sample - loss: 0.0490 - accuracy: 0.9895 - val_loss: 0.1066 - val_accuracy: 0.9647
+Train on 2382 samples, validate on 732 samples
+Epoch 1/30
+
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2382/2382 [==============================] - 1s 594us/sample - loss: 1.8395 - accuracy: 0.3963 - val_loss: 0.8886 - val_accuracy: 0.6503
+Epoch 2/30
+
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2382/2382 [==============================] - 1s 384us/sample - loss: 0.8633 - accuracy: 0.6713 - val_loss: 0.6477 - val_accuracy: 0.7787
+Epoch 3/30
+
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2382/2382 [==============================] - 1s 385us/sample - loss: 0.5933 - accuracy: 0.7922 - val_loss: 0.4720 - val_accuracy: 0.8648
+Epoch 4/30
+
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2382/2382 [==============================] - 1s 381us/sample - loss: 0.4350 - accuracy: 0.8472 - val_loss: 0.3952 - val_accuracy: 0.8757
+Epoch 5/30
+
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2382/2382 [==============================] - 1s 385us/sample - loss: 0.3548 - accuracy: 0.8804 - val_loss: 0.3334 - val_accuracy: 0.9085
+Epoch 6/30
+
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2382/2382 [==============================] - 1s 383us/sample - loss: 0.2830 - accuracy: 0.9081 - val_loss: 0.3012 - val_accuracy: 0.9167
+Epoch 7/30
+
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2382/2382 [==============================] - 1s 383us/sample - loss: 0.2402 - accuracy: 0.9312 - val_loss: 0.2628 - val_accuracy: 0.9139
+Epoch 8/30
+
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2382/2382 [==============================] - 1s 383us/sample - loss: 0.1998 - accuracy: 0.9437 - val_loss: 0.2436 - val_accuracy: 0.9153
+Epoch 9/30
+
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2382/2382 [==============================] - 1s 383us/sample - loss: 0.1787 - accuracy: 0.9517 - val_loss: 0.2305 - val_accuracy: 0.9276
+Epoch 10/30
+
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2382/2382 [==============================] - 1s 381us/sample - loss: 0.1513 - accuracy: 0.9547 - val_loss: 0.2143 - val_accuracy: 0.9331
+Epoch 11/30
+
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2382/2382 [==============================] - 1s 388us/sample - loss: 0.1408 - accuracy: 0.9589 - val_loss: 0.2035 - val_accuracy: 0.9317
+Epoch 12/30
+
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2382/2382 [==============================] - 1s 381us/sample - loss: 0.1298 - accuracy: 0.9605 - val_loss: 0.2059 - val_accuracy: 0.9290
+Epoch 13/30
+
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2382/2382 [==============================] - 1s 378us/sample - loss: 0.1220 - accuracy: 0.9668 - val_loss: 0.1932 - val_accuracy: 0.9317
+Epoch 14/30
+
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2382/2382 [==============================] - 1s 384us/sample - loss: 0.1006 - accuracy: 0.9744 - val_loss: 0.1894 - val_accuracy: 0.9303
+Epoch 15/30
+
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2382/2382 [==============================] - 1s 383us/sample - loss: 0.0844 - accuracy: 0.9773 - val_loss: 0.1769 - val_accuracy: 0.9399
+Epoch 16/30
+
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2382/2382 [==============================] - 1s 380us/sample - loss: 0.0791 - accuracy: 0.9807 - val_loss: 0.1894 - val_accuracy: 0.9303
+Epoch 17/30
+
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2382/2382 [==============================] - 1s 382us/sample - loss: 0.0758 - accuracy: 0.9815 - val_loss: 0.1703 - val_accuracy: 0.9358
+Epoch 18/30
+
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2382/2382 [==============================] - 1s 383us/sample - loss: 0.0692 - accuracy: 0.9832 - val_loss: 0.1709 - val_accuracy: 0.9344
+Epoch 19/30
+
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2382/2382 [==============================] - 1s 378us/sample - loss: 0.0639 - accuracy: 0.9849 - val_loss: 0.1626 - val_accuracy: 0.9426
+Epoch 20/30
+
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2382/2382 [==============================] - 1s 380us/sample - loss: 0.0602 - accuracy: 0.9832 - val_loss: 0.1621 - val_accuracy: 0.9413
+Epoch 21/30
+
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2382/2382 [==============================] - 1s 382us/sample - loss: 0.0494 - accuracy: 0.9916 - val_loss: 0.1533 - val_accuracy: 0.9454
+Epoch 22/30
+
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2382/2382 [==============================] - 1s 381us/sample - loss: 0.0576 - accuracy: 0.9866 - val_loss: 0.1491 - val_accuracy: 0.9440
+Epoch 23/30
+
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2382/2382 [==============================] - 1s 381us/sample - loss: 0.0483 - accuracy: 0.9882 - val_loss: 0.1536 - val_accuracy: 0.9440
+Epoch 24/30
+
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2382/2382 [==============================] - 1s 379us/sample - loss: 0.0469 - accuracy: 0.9882 - val_loss: 0.1493 - val_accuracy: 0.9495
+Epoch 25/30
+
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2382/2382 [==============================] - 1s 380us/sample - loss: 0.0433 - accuracy: 0.9903 - val_loss: 0.1516 - val_accuracy: 0.9508
+Epoch 26/30
+
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2382/2382 [==============================] - 1s 379us/sample - loss: 0.0343 - accuracy: 0.9945 - val_loss: 0.1428 - val_accuracy: 0.9508
+Epoch 27/30
+
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2382/2382 [==============================] - 1s 379us/sample - loss: 0.0347 - accuracy: 0.9941 - val_loss: 0.1487 - val_accuracy: 0.9495
+Epoch 28/30
+
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2382/2382 [==============================] - 1s 382us/sample - loss: 0.0369 - accuracy: 0.9929 - val_loss: 0.1474 - val_accuracy: 0.9495
+Epoch 29/30
+
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2382/2382 [==============================] - 1s 381us/sample - loss: 0.0362 - accuracy: 0.9933 - val_loss: 0.1523 - val_accuracy: 0.9385
+Epoch 30/30
+
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2382/2382 [==============================] - 1s 389us/sample - loss: 0.0291 - accuracy: 0.9945 - val_loss: 0.1534 - val_accuracy: 0.9440
+Train on 709 samples, validate on 2097 samples
+Epoch 1/30
+
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709/709 [==============================] - 1s 2ms/sample - loss: 2.9956 - accuracy: 0.2609 - val_loss: 1.7541 - val_accuracy: 0.3124
+Epoch 2/30
+
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709/709 [==============================] - 1s 877us/sample - loss: 2.0736 - accuracy: 0.3611 - val_loss: 1.3705 - val_accuracy: 0.5045
+Epoch 3/30
+
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709/709 [==============================] - 1s 885us/sample - loss: 1.3934 - accuracy: 0.5007 - val_loss: 0.9677 - val_accuracy: 0.6438
+Epoch 4/30
+
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709/709 [==============================] - 1s 854us/sample - loss: 1.0615 - accuracy: 0.6065 - val_loss: 0.8707 - val_accuracy: 0.6805
+Epoch 5/30
+
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709/709 [==============================] - 1s 858us/sample - loss: 0.8832 - accuracy: 0.6615 - val_loss: 0.7352 - val_accuracy: 0.7549
+Epoch 6/30
+
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709/709 [==============================] - 1s 851us/sample - loss: 0.7345 - accuracy: 0.7334 - val_loss: 0.6827 - val_accuracy: 0.7783
+Epoch 7/30
+
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709/709 [==============================] - 1s 851us/sample - loss: 0.6475 - accuracy: 0.7602 - val_loss: 0.6049 - val_accuracy: 0.8031
+Epoch 8/30
+
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709/709 [==============================] - 1s 868us/sample - loss: 0.5581 - accuracy: 0.8195 - val_loss: 0.5680 - val_accuracy: 0.8193
+Epoch 9/30
+
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709/709 [==============================] - 1s 849us/sample - loss: 0.5027 - accuracy: 0.8336 - val_loss: 0.5379 - val_accuracy: 0.8312
+Epoch 10/30
+
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709/709 [==============================] - 1s 847us/sample - loss: 0.4551 - accuracy: 0.8491 - val_loss: 0.5009 - val_accuracy: 0.8526
+Epoch 11/30
+
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709/709 [==============================] - 1s 850us/sample - loss: 0.4460 - accuracy: 0.8463 - val_loss: 0.4725 - val_accuracy: 0.8646
+Epoch 12/30
+
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709/709 [==============================] - 1s 852us/sample - loss: 0.4118 - accuracy: 0.8787 - val_loss: 0.4696 - val_accuracy: 0.8612
+Epoch 13/30
+
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709/709 [==============================] - 1s 849us/sample - loss: 0.3738 - accuracy: 0.8872 - val_loss: 0.4349 - val_accuracy: 0.8722
+Epoch 14/30
+
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709/709 [==============================] - 1s 852us/sample - loss: 0.3263 - accuracy: 0.9126 - val_loss: 0.4255 - val_accuracy: 0.8665
+Epoch 15/30
+
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709/709 [==============================] - 1s 852us/sample - loss: 0.3074 - accuracy: 0.9083 - val_loss: 0.3957 - val_accuracy: 0.8879
+Epoch 16/30
+
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709/709 [==============================] - 1s 854us/sample - loss: 0.2699 - accuracy: 0.9379 - val_loss: 0.3854 - val_accuracy: 0.8889
+Epoch 17/30
+
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709/709 [==============================] - 1s 847us/sample - loss: 0.2394 - accuracy: 0.9506 - val_loss: 0.3646 - val_accuracy: 0.8946
+Epoch 18/30
+
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448/709 [=================>............] - ETA: 0s - loss: 0.2101 - accuracy: 0.9554
640/709 [==========================>...] - ETA: 0s - loss: 0.2112 - accuracy: 0.9484
709/709 [==============================] - 1s 852us/sample - loss: 0.2198 - accuracy: 0.9422 - val_loss: 0.3653 - val_accuracy: 0.8898
+Epoch 19/30
+
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256/709 [=========>....................] - ETA: 0s - loss: 0.2116 - accuracy: 0.9375
448/709 [=================>............] - ETA: 0s - loss: 0.2209 - accuracy: 0.9330
640/709 [==========================>...] - ETA: 0s - loss: 0.2318 - accuracy: 0.9281
709/709 [==============================] - 1s 845us/sample - loss: 0.2251 - accuracy: 0.9309 - val_loss: 0.3479 - val_accuracy: 0.9027
+Epoch 20/30
+
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256/709 [=========>....................] - ETA: 0s - loss: 0.1827 - accuracy: 0.9609
448/709 [=================>............] - ETA: 0s - loss: 0.1940 - accuracy: 0.9509
640/709 [==========================>...] - ETA: 0s - loss: 0.1966 - accuracy: 0.9484
709/709 [==============================] - 1s 850us/sample - loss: 0.1934 - accuracy: 0.9520 - val_loss: 0.3345 - val_accuracy: 0.9022
+Epoch 21/30
+
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709/709 [==============================] - 1s 852us/sample - loss: 0.1823 - accuracy: 0.9492 - val_loss: 0.3342 - val_accuracy: 0.9032
+Epoch 22/30
+
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256/709 [=========>....................] - ETA: 0s - loss: 0.1798 - accuracy: 0.9492
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640/709 [==========================>...] - ETA: 0s - loss: 0.1889 - accuracy: 0.9406
709/709 [==============================] - 1s 856us/sample - loss: 0.1836 - accuracy: 0.9436 - val_loss: 0.3202 - val_accuracy: 0.9065
+Epoch 23/30
+
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256/709 [=========>....................] - ETA: 0s - loss: 0.1609 - accuracy: 0.9648
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640/709 [==========================>...] - ETA: 0s - loss: 0.1764 - accuracy: 0.9484
709/709 [==============================] - 1s 852us/sample - loss: 0.1751 - accuracy: 0.9478 - val_loss: 0.3063 - val_accuracy: 0.9089
+Epoch 24/30
+
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640/709 [==========================>...] - ETA: 0s - loss: 0.1621 - accuracy: 0.9578
709/709 [==============================] - 1s 859us/sample - loss: 0.1611 - accuracy: 0.9591 - val_loss: 0.3277 - val_accuracy: 0.8994
+Epoch 25/30
+
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709/709 [==============================] - 1s 851us/sample - loss: 0.1645 - accuracy: 0.9520 - val_loss: 0.3050 - val_accuracy: 0.9113
+Epoch 26/30
+
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709/709 [==============================] - 1s 846us/sample - loss: 0.1586 - accuracy: 0.9633 - val_loss: 0.2876 - val_accuracy: 0.9165
+Epoch 27/30
+
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256/709 [=========>....................] - ETA: 0s - loss: 0.1352 - accuracy: 0.9648
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640/709 [==========================>...] - ETA: 0s - loss: 0.1369 - accuracy: 0.9672
709/709 [==============================] - 1s 852us/sample - loss: 0.1542 - accuracy: 0.9577 - val_loss: 0.2992 - val_accuracy: 0.9089
+Epoch 28/30
+
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256/709 [=========>....................] - ETA: 0s - loss: 0.1408 - accuracy: 0.9688
448/709 [=================>............] - ETA: 0s - loss: 0.1316 - accuracy: 0.9688
640/709 [==========================>...] - ETA: 0s - loss: 0.1387 - accuracy: 0.9625
709/709 [==============================] - 1s 850us/sample - loss: 0.1343 - accuracy: 0.9661 - val_loss: 0.2853 - val_accuracy: 0.9156
+Epoch 29/30
+
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256/709 [=========>....................] - ETA: 0s - loss: 0.1555 - accuracy: 0.9609
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640/709 [==========================>...] - ETA: 0s - loss: 0.1370 - accuracy: 0.9688
709/709 [==============================] - 1s 851us/sample - loss: 0.1339 - accuracy: 0.9676 - val_loss: 0.2960 - val_accuracy: 0.9123
+Epoch 30/30
+
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256/709 [=========>....................] - ETA: 0s - loss: 0.1430 - accuracy: 0.9570
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640/709 [==========================>...] - ETA: 0s - loss: 0.1315 - accuracy: 0.9625
709/709 [==============================] - 1s 850us/sample - loss: 0.1352 - accuracy: 0.9633 - val_loss: 0.2866 - val_accuracy: 0.9103
+Train on 732 samples, validate on 2382 samples
+Epoch 1/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 3.6670 - accuracy: 0.1797
448/732 [=================>............] - ETA: 0s - loss: 3.1352 - accuracy: 0.2076
640/732 [=========================>....] - ETA: 0s - loss: 2.9882 - accuracy: 0.2313
732/732 [==============================] - 1s 2ms/sample - loss: 2.8779 - accuracy: 0.2486 - val_loss: 1.9611 - val_accuracy: 0.3237
+Epoch 2/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 2.2582 - accuracy: 0.2930
448/732 [=================>............] - ETA: 0s - loss: 2.0666 - accuracy: 0.3214
640/732 [=========================>....] - ETA: 0s - loss: 1.9426 - accuracy: 0.3469
732/732 [==============================] - 1s 900us/sample - loss: 1.9162 - accuracy: 0.3511 - val_loss: 1.3675 - val_accuracy: 0.4412
+Epoch 3/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 1.5953 - accuracy: 0.3984
448/732 [=================>............] - ETA: 0s - loss: 1.5105 - accuracy: 0.4107
640/732 [=========================>....] - ETA: 0s - loss: 1.4454 - accuracy: 0.4297
732/732 [==============================] - 1s 902us/sample - loss: 1.4199 - accuracy: 0.4399 - val_loss: 1.1096 - val_accuracy: 0.5638
+Epoch 4/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 1.0828 - accuracy: 0.6211
448/732 [=================>............] - ETA: 0s - loss: 1.0518 - accuracy: 0.6138
640/732 [=========================>....] - ETA: 0s - loss: 1.0199 - accuracy: 0.6109
732/732 [==============================] - 1s 900us/sample - loss: 0.9961 - accuracy: 0.6161 - val_loss: 0.9531 - val_accuracy: 0.6369
+Epoch 5/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 0.9349 - accuracy: 0.6406
448/732 [=================>............] - ETA: 0s - loss: 0.9557 - accuracy: 0.6295
640/732 [=========================>....] - ETA: 0s - loss: 0.9193 - accuracy: 0.6422
732/732 [==============================] - 1s 897us/sample - loss: 0.9073 - accuracy: 0.6475 - val_loss: 0.8592 - val_accuracy: 0.6784
+Epoch 6/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 0.7746 - accuracy: 0.6914
448/732 [=================>............] - ETA: 0s - loss: 0.7502 - accuracy: 0.6987
640/732 [=========================>....] - ETA: 0s - loss: 0.7538 - accuracy: 0.6953
732/732 [==============================] - 1s 898us/sample - loss: 0.7559 - accuracy: 0.6954 - val_loss: 0.7908 - val_accuracy: 0.7091
+Epoch 7/30
+
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448/732 [=================>............] - ETA: 0s - loss: 0.6514 - accuracy: 0.7634
640/732 [=========================>....] - ETA: 0s - loss: 0.6661 - accuracy: 0.7531
732/732 [==============================] - 1s 903us/sample - loss: 0.6559 - accuracy: 0.7609 - val_loss: 0.7408 - val_accuracy: 0.7263
+Epoch 8/30
+
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448/732 [=================>............] - ETA: 0s - loss: 0.5900 - accuracy: 0.8036
640/732 [=========================>....] - ETA: 0s - loss: 0.6091 - accuracy: 0.7875
732/732 [==============================] - 1s 906us/sample - loss: 0.5939 - accuracy: 0.7937 - val_loss: 0.6784 - val_accuracy: 0.7540
+Epoch 9/30
+
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640/732 [=========================>....] - ETA: 0s - loss: 0.5038 - accuracy: 0.8219
732/732 [==============================] - 1s 903us/sample - loss: 0.4891 - accuracy: 0.8292 - val_loss: 0.6671 - val_accuracy: 0.7586
+Epoch 10/30
+
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448/732 [=================>............] - ETA: 0s - loss: 0.4801 - accuracy: 0.8438
640/732 [=========================>....] - ETA: 0s - loss: 0.4880 - accuracy: 0.8359
732/732 [==============================] - 1s 896us/sample - loss: 0.4783 - accuracy: 0.8374 - val_loss: 0.5964 - val_accuracy: 0.7918
+Epoch 11/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 0.4559 - accuracy: 0.8281
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640/732 [=========================>....] - ETA: 0s - loss: 0.4437 - accuracy: 0.8547
732/732 [==============================] - 1s 888us/sample - loss: 0.4425 - accuracy: 0.8566 - val_loss: 0.5872 - val_accuracy: 0.7897
+Epoch 12/30
+
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448/732 [=================>............] - ETA: 0s - loss: 0.3910 - accuracy: 0.8772
640/732 [=========================>....] - ETA: 0s - loss: 0.3712 - accuracy: 0.8797
732/732 [==============================] - 1s 895us/sample - loss: 0.3773 - accuracy: 0.8730 - val_loss: 0.5412 - val_accuracy: 0.8090
+Epoch 13/30
+
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640/732 [=========================>....] - ETA: 0s - loss: 0.3580 - accuracy: 0.8719
732/732 [==============================] - 1s 899us/sample - loss: 0.3453 - accuracy: 0.8798 - val_loss: 0.5244 - val_accuracy: 0.8165
+Epoch 14/30
+
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640/732 [=========================>....] - ETA: 0s - loss: 0.2864 - accuracy: 0.9125
732/732 [==============================] - 1s 895us/sample - loss: 0.2913 - accuracy: 0.9085 - val_loss: 0.4997 - val_accuracy: 0.8291
+Epoch 15/30
+
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448/732 [=================>............] - ETA: 0s - loss: 0.2583 - accuracy: 0.9330
640/732 [=========================>....] - ETA: 0s - loss: 0.2827 - accuracy: 0.9219
732/732 [==============================] - 1s 897us/sample - loss: 0.2864 - accuracy: 0.9235 - val_loss: 0.4843 - val_accuracy: 0.8317
+Epoch 16/30
+
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448/732 [=================>............] - ETA: 0s - loss: 0.2932 - accuracy: 0.8884
640/732 [=========================>....] - ETA: 0s - loss: 0.2864 - accuracy: 0.8969
732/732 [==============================] - 1s 889us/sample - loss: 0.2745 - accuracy: 0.9030 - val_loss: 0.4626 - val_accuracy: 0.8421
+Epoch 17/30
+
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448/732 [=================>............] - ETA: 0s - loss: 0.2551 - accuracy: 0.9263
640/732 [=========================>....] - ETA: 0s - loss: 0.2529 - accuracy: 0.9328
732/732 [==============================] - 1s 887us/sample - loss: 0.2450 - accuracy: 0.9331 - val_loss: 0.4709 - val_accuracy: 0.8325
+Epoch 18/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 0.2277 - accuracy: 0.9414
448/732 [=================>............] - ETA: 0s - loss: 0.2314 - accuracy: 0.9286
640/732 [=========================>....] - ETA: 0s - loss: 0.2193 - accuracy: 0.9406
732/732 [==============================] - 1s 902us/sample - loss: 0.2119 - accuracy: 0.9454 - val_loss: 0.4243 - val_accuracy: 0.8560
+Epoch 19/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 0.2109 - accuracy: 0.9531
448/732 [=================>............] - ETA: 0s - loss: 0.1923 - accuracy: 0.9621
640/732 [=========================>....] - ETA: 0s - loss: 0.2091 - accuracy: 0.9500
732/732 [==============================] - 1s 897us/sample - loss: 0.2033 - accuracy: 0.9495 - val_loss: 0.4472 - val_accuracy: 0.8380
+Epoch 20/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 0.1818 - accuracy: 0.9531
448/732 [=================>............] - ETA: 0s - loss: 0.2028 - accuracy: 0.9420
640/732 [=========================>....] - ETA: 0s - loss: 0.1961 - accuracy: 0.9453
732/732 [==============================] - 1s 893us/sample - loss: 0.2006 - accuracy: 0.9426 - val_loss: 0.4121 - val_accuracy: 0.8547
+Epoch 21/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 0.2020 - accuracy: 0.9492
448/732 [=================>............] - ETA: 0s - loss: 0.1880 - accuracy: 0.9464
640/732 [=========================>....] - ETA: 0s - loss: 0.1867 - accuracy: 0.9484
732/732 [==============================] - 1s 895us/sample - loss: 0.1879 - accuracy: 0.9481 - val_loss: 0.4384 - val_accuracy: 0.8405
+Epoch 22/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 0.1719 - accuracy: 0.9492
448/732 [=================>............] - ETA: 0s - loss: 0.1648 - accuracy: 0.9509
640/732 [=========================>....] - ETA: 0s - loss: 0.1588 - accuracy: 0.9578
732/732 [==============================] - 1s 890us/sample - loss: 0.1573 - accuracy: 0.9617 - val_loss: 0.3932 - val_accuracy: 0.8640
+Epoch 23/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 0.1582 - accuracy: 0.9688
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640/732 [=========================>....] - ETA: 0s - loss: 0.1522 - accuracy: 0.9609
732/732 [==============================] - 1s 944us/sample - loss: 0.1461 - accuracy: 0.9658 - val_loss: 0.4017 - val_accuracy: 0.8573
+Epoch 24/30
+
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640/732 [=========================>....] - ETA: 0s - loss: 0.1537 - accuracy: 0.9578
732/732 [==============================] - 1s 941us/sample - loss: 0.1557 - accuracy: 0.9563 - val_loss: 0.3835 - val_accuracy: 0.8673
+Epoch 25/30
+
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448/732 [=================>............] - ETA: 0s - loss: 0.1374 - accuracy: 0.9621
640/732 [=========================>....] - ETA: 0s - loss: 0.1360 - accuracy: 0.9672
732/732 [==============================] - 1s 902us/sample - loss: 0.1382 - accuracy: 0.9686 - val_loss: 0.3780 - val_accuracy: 0.8673
+Epoch 26/30
+
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640/732 [=========================>....] - ETA: 0s - loss: 0.1428 - accuracy: 0.9688
732/732 [==============================] - 1s 893us/sample - loss: 0.1489 - accuracy: 0.9631 - val_loss: 0.3609 - val_accuracy: 0.8745
+Epoch 27/30
+
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256/732 [=========>....................] - ETA: 0s - loss: 0.1211 - accuracy: 0.9688
448/732 [=================>............] - ETA: 0s - loss: 0.1181 - accuracy: 0.9754
640/732 [=========================>....] - ETA: 0s - loss: 0.1244 - accuracy: 0.9719
732/732 [==============================] - 1s 895us/sample - loss: 0.1237 - accuracy: 0.9727 - val_loss: 0.3827 - val_accuracy: 0.8627
+Epoch 28/30
+
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640/732 [=========================>....] - ETA: 0s - loss: 0.1154 - accuracy: 0.9781
732/732 [==============================] - 1s 900us/sample - loss: 0.1185 - accuracy: 0.9768 - val_loss: 0.3697 - val_accuracy: 0.8686
+Epoch 29/30
+
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640/732 [=========================>....] - ETA: 0s - loss: 0.1102 - accuracy: 0.9719
732/732 [==============================] - 1s 901us/sample - loss: 0.1176 - accuracy: 0.9672 - val_loss: 0.3426 - val_accuracy: 0.8820
+Epoch 30/30
+
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732/732 [==============================] - 1s 922us/sample - loss: 0.1279 - accuracy: 0.9590 - val_loss: 0.3968 - val_accuracy: 0.8489
+Train on 2123 samples, validate on 683 samples
+Epoch 1/30
+
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2123/2123 [==============================] - 1s 665us/sample - loss: 2.2322 - accuracy: 0.3293 - val_loss: 0.9837 - val_accuracy: 0.6047
+Epoch 2/30
+
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2123/2123 [==============================] - 1s 388us/sample - loss: 1.0447 - accuracy: 0.6001 - val_loss: 0.6251 - val_accuracy: 0.7965
+Epoch 3/30
+
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2123/2123 [==============================] - 1s 399us/sample - loss: 0.6744 - accuracy: 0.7513 - val_loss: 0.4458 - val_accuracy: 0.8902
+Epoch 4/30
+
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2123/2123 [==============================] - 1s 385us/sample - loss: 0.5125 - accuracy: 0.8139 - val_loss: 0.3369 - val_accuracy: 0.9356
+Epoch 5/30
+
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2123/2123 [==============================] - 1s 387us/sample - loss: 0.4105 - accuracy: 0.8577 - val_loss: 0.2761 - val_accuracy: 0.9444
+Epoch 6/30
+
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2123/2123 [==============================] - 1s 385us/sample - loss: 0.3428 - accuracy: 0.8874 - val_loss: 0.2397 - val_accuracy: 0.9488
+Epoch 7/30
+
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2123/2123 [==============================] - 1s 388us/sample - loss: 0.3026 - accuracy: 0.9020 - val_loss: 0.2156 - val_accuracy: 0.9561
+Epoch 8/30
+
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2123/2123 [==============================] - 1s 387us/sample - loss: 0.2535 - accuracy: 0.9223 - val_loss: 0.1767 - val_accuracy: 0.9619
+Epoch 9/30
+
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2123/2123 [==============================] - 1s 383us/sample - loss: 0.2182 - accuracy: 0.9341 - val_loss: 0.1752 - val_accuracy: 0.9634
+Epoch 10/30
+
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2123/2123 [==============================] - 1s 386us/sample - loss: 0.1925 - accuracy: 0.9425 - val_loss: 0.1593 - val_accuracy: 0.9634
+Epoch 11/30
+
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2123/2123 [==============================] - 1s 396us/sample - loss: 0.1828 - accuracy: 0.9416 - val_loss: 0.1426 - val_accuracy: 0.9693
+Epoch 12/30
+
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2123/2123 [==============================] - 1s 386us/sample - loss: 0.1577 - accuracy: 0.9491 - val_loss: 0.1365 - val_accuracy: 0.9678
+Epoch 13/30
+
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2123/2123 [==============================] - 1s 385us/sample - loss: 0.1333 - accuracy: 0.9637 - val_loss: 0.1327 - val_accuracy: 0.9678
+Epoch 14/30
+
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2123/2123 [==============================] - 1s 399us/sample - loss: 0.1271 - accuracy: 0.9557 - val_loss: 0.1387 - val_accuracy: 0.9619
+Epoch 15/30
+
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2123/2123 [==============================] - 1s 383us/sample - loss: 0.1148 - accuracy: 0.9666 - val_loss: 0.1517 - val_accuracy: 0.9561
+Epoch 16/30
+
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2123/2123 [==============================] - 1s 388us/sample - loss: 0.1078 - accuracy: 0.9703 - val_loss: 0.1179 - val_accuracy: 0.9751
+Epoch 17/30
+
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2123/2123 [==============================] - 1s 386us/sample - loss: 0.1066 - accuracy: 0.9680 - val_loss: 0.1164 - val_accuracy: 0.9722
+Epoch 18/30
+
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2123/2123 [==============================] - 1s 382us/sample - loss: 0.0939 - accuracy: 0.9736 - val_loss: 0.1109 - val_accuracy: 0.9678
+Epoch 19/30
+
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2123/2123 [==============================] - 1s 388us/sample - loss: 0.0844 - accuracy: 0.9769 - val_loss: 0.1086 - val_accuracy: 0.9736
+Epoch 20/30
+
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2123/2123 [==============================] - 1s 384us/sample - loss: 0.0837 - accuracy: 0.9788 - val_loss: 0.0995 - val_accuracy: 0.9766
+Epoch 21/30
+
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2123/2123 [==============================] - 1s 388us/sample - loss: 0.0806 - accuracy: 0.9732 - val_loss: 0.1031 - val_accuracy: 0.9722
+Epoch 22/30
+
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2123/2123 [==============================] - 1s 386us/sample - loss: 0.0723 - accuracy: 0.9793 - val_loss: 0.0983 - val_accuracy: 0.9751
+Epoch 23/30
+
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2123/2123 [==============================] - 1s 389us/sample - loss: 0.0677 - accuracy: 0.9826 - val_loss: 0.0960 - val_accuracy: 0.9766
+Epoch 24/30
+
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2123/2123 [==============================] - 1s 389us/sample - loss: 0.0649 - accuracy: 0.9793 - val_loss: 0.1053 - val_accuracy: 0.9707
+Epoch 25/30
+
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2123/2123 [==============================] - 1s 391us/sample - loss: 0.0601 - accuracy: 0.9812 - val_loss: 0.0933 - val_accuracy: 0.9693
+Epoch 26/30
+
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2123/2123 [==============================] - 1s 387us/sample - loss: 0.0578 - accuracy: 0.9859 - val_loss: 0.0970 - val_accuracy: 0.9751
+Epoch 27/30
+
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2123/2123 [==============================] - 1s 384us/sample - loss: 0.0522 - accuracy: 0.9873 - val_loss: 0.0858 - val_accuracy: 0.9780
+Epoch 28/30
+
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2123/2123 [==============================] - 1s 386us/sample - loss: 0.0499 - accuracy: 0.9896 - val_loss: 0.1041 - val_accuracy: 0.9707
+Epoch 29/30
+
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2123/2123 [==============================] - 1s 386us/sample - loss: 0.0478 - accuracy: 0.9887 - val_loss: 0.0965 - val_accuracy: 0.9736
+Epoch 30/30
+
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2123/2123 [==============================] - 1s 387us/sample - loss: 0.0444 - accuracy: 0.9901 - val_loss: 0.0858 - val_accuracy: 0.9780
+Train on 2358 samples, validate on 756 samples
+Epoch 1/30
+
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2358/2358 [==============================] - 1s 607us/sample - loss: 2.4994 - accuracy: 0.3312 - val_loss: 1.1710 - val_accuracy: 0.5278
+Epoch 2/30
+
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2358/2358 [==============================] - 1s 387us/sample - loss: 1.2084 - accuracy: 0.5581 - val_loss: 0.7591 - val_accuracy: 0.7249
+Epoch 3/30
+
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2358/2358 [==============================] - 1s 389us/sample - loss: 0.8016 - accuracy: 0.6955 - val_loss: 0.5735 - val_accuracy: 0.8135
+Epoch 4/30
+
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2358/2358 [==============================] - 1s 382us/sample - loss: 0.6048 - accuracy: 0.7731 - val_loss: 0.4998 - val_accuracy: 0.8386
+Epoch 5/30
+
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2358/2358 [==============================] - 1s 385us/sample - loss: 0.5008 - accuracy: 0.8189 - val_loss: 0.4136 - val_accuracy: 0.8730
+Epoch 6/30
+
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2358/2358 [==============================] - 1s 381us/sample - loss: 0.4233 - accuracy: 0.8482 - val_loss: 0.3494 - val_accuracy: 0.9101
+Epoch 7/30
+
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2358/2358 [==============================] - 1s 383us/sample - loss: 0.3409 - accuracy: 0.8868 - val_loss: 0.2879 - val_accuracy: 0.9180
+Epoch 8/30
+
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2358/2358 [==============================] - 1s 384us/sample - loss: 0.3023 - accuracy: 0.9008 - val_loss: 0.2350 - val_accuracy: 0.9405
+Epoch 9/30
+
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2358/2358 [==============================] - 1s 383us/sample - loss: 0.2702 - accuracy: 0.9126 - val_loss: 0.2486 - val_accuracy: 0.9312
+Epoch 10/30
+
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2358/2358 [==============================] - 1s 380us/sample - loss: 0.2230 - accuracy: 0.9288 - val_loss: 0.2016 - val_accuracy: 0.9484
+Epoch 11/30
+
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2358/2358 [==============================] - 1s 381us/sample - loss: 0.2051 - accuracy: 0.9338 - val_loss: 0.1756 - val_accuracy: 0.9511
+Epoch 12/30
+
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2358/2358 [==============================] - 1s 385us/sample - loss: 0.1791 - accuracy: 0.9453 - val_loss: 0.1573 - val_accuracy: 0.9616
+Epoch 13/30
+
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2358/2358 [==============================] - 1s 381us/sample - loss: 0.1639 - accuracy: 0.9474 - val_loss: 0.1901 - val_accuracy: 0.9339
+Epoch 14/30
+
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2358/2358 [==============================] - 1s 383us/sample - loss: 0.1542 - accuracy: 0.9525 - val_loss: 0.1565 - val_accuracy: 0.9550
+Epoch 15/30
+
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2358/2358 [==============================] - 1s 384us/sample - loss: 0.1392 - accuracy: 0.9584 - val_loss: 0.1344 - val_accuracy: 0.9603
+Epoch 16/30
+
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2358/2358 [==============================] - 1s 385us/sample - loss: 0.1197 - accuracy: 0.9707 - val_loss: 0.1213 - val_accuracy: 0.9669
+Epoch 17/30
+
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2358/2358 [==============================] - 1s 378us/sample - loss: 0.1121 - accuracy: 0.9669 - val_loss: 0.1150 - val_accuracy: 0.9683
+Epoch 18/30
+
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2358/2358 [==============================] - 1s 380us/sample - loss: 0.0986 - accuracy: 0.9707 - val_loss: 0.1338 - val_accuracy: 0.9563
+Epoch 19/30
+
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2358/2358 [==============================] - 1s 383us/sample - loss: 0.0864 - accuracy: 0.9775 - val_loss: 0.1158 - val_accuracy: 0.9669
+Epoch 20/30
+
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2358/2358 [==============================] - 1s 380us/sample - loss: 0.0906 - accuracy: 0.9750 - val_loss: 0.1020 - val_accuracy: 0.9709
+Epoch 21/30
+
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2358/2358 [==============================] - 1s 383us/sample - loss: 0.0870 - accuracy: 0.9758 - val_loss: 0.1164 - val_accuracy: 0.9630
+Epoch 22/30
+
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2358/2358 [==============================] - 1s 389us/sample - loss: 0.0745 - accuracy: 0.9792 - val_loss: 0.1057 - val_accuracy: 0.9709
+Epoch 23/30
+
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2358/2358 [==============================] - 1s 383us/sample - loss: 0.0808 - accuracy: 0.9771 - val_loss: 0.1056 - val_accuracy: 0.9696
+Epoch 24/30
+
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2358/2358 [==============================] - 1s 381us/sample - loss: 0.0693 - accuracy: 0.9788 - val_loss: 0.1014 - val_accuracy: 0.9669
+Epoch 25/30
+
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2358/2358 [==============================] - 1s 383us/sample - loss: 0.0607 - accuracy: 0.9847 - val_loss: 0.0945 - val_accuracy: 0.9735
+Epoch 26/30
+
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2358/2358 [==============================] - 1s 381us/sample - loss: 0.0572 - accuracy: 0.9869 - val_loss: 0.0977 - val_accuracy: 0.9683
+Epoch 27/30
+
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2358/2358 [==============================] - 1s 380us/sample - loss: 0.0510 - accuracy: 0.9890 - val_loss: 0.1048 - val_accuracy: 0.9683
+Epoch 28/30
+
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2358/2358 [==============================] - 1s 379us/sample - loss: 0.0538 - accuracy: 0.9847 - val_loss: 0.0905 - val_accuracy: 0.9696
+Epoch 29/30
+
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2358/2358 [==============================] - 1s 381us/sample - loss: 0.0472 - accuracy: 0.9911 - val_loss: 0.0852 - val_accuracy: 0.9722
+Epoch 30/30
+
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2358/2358 [==============================] - 1s 380us/sample - loss: 0.0450 - accuracy: 0.9907 - val_loss: 0.0985 - val_accuracy: 0.9709
+Train on 683 samples, validate on 2123 samples
+Epoch 1/30
+
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256/683 [==========>...................] - ETA: 0s - loss: 3.3225 - accuracy: 0.2031
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683/683 [==============================] - 1s 2ms/sample - loss: 2.8063 - accuracy: 0.2635 - val_loss: 1.9025 - val_accuracy: 0.3057
+Epoch 2/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 2.2259 - accuracy: 0.3266
683/683 [==============================] - 1s 872us/sample - loss: 2.2094 - accuracy: 0.3250 - val_loss: 1.4412 - val_accuracy: 0.4470
+Epoch 3/30
+
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683/683 [==============================] - 1s 876us/sample - loss: 1.4675 - accuracy: 0.4510 - val_loss: 1.2352 - val_accuracy: 0.5219
+Epoch 4/30
+
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683/683 [==============================] - 1s 871us/sample - loss: 1.0955 - accuracy: 0.5915 - val_loss: 1.0717 - val_accuracy: 0.5987
+Epoch 5/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.8784 - accuracy: 0.6656
683/683 [==============================] - 1s 870us/sample - loss: 0.8897 - accuracy: 0.6676 - val_loss: 0.9458 - val_accuracy: 0.6528
+Epoch 6/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.7422 - accuracy: 0.7266
683/683 [==============================] - 1s 880us/sample - loss: 0.7455 - accuracy: 0.7233 - val_loss: 0.8792 - val_accuracy: 0.6825
+Epoch 7/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.6395 - accuracy: 0.7563
683/683 [==============================] - 1s 882us/sample - loss: 0.6403 - accuracy: 0.7540 - val_loss: 0.8032 - val_accuracy: 0.7146
+Epoch 8/30
+
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683/683 [==============================] - 1s 873us/sample - loss: 0.5818 - accuracy: 0.7789 - val_loss: 0.7340 - val_accuracy: 0.7461
+Epoch 9/30
+
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683/683 [==============================] - 1s 870us/sample - loss: 0.4875 - accuracy: 0.8433 - val_loss: 0.6984 - val_accuracy: 0.7626
+Epoch 10/30
+
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683/683 [==============================] - 1s 874us/sample - loss: 0.4428 - accuracy: 0.8492 - val_loss: 0.6403 - val_accuracy: 0.7909
+Epoch 11/30
+
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683/683 [==============================] - 1s 875us/sample - loss: 0.4016 - accuracy: 0.8726 - val_loss: 0.6183 - val_accuracy: 0.7937
+Epoch 12/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.3519 - accuracy: 0.8953
683/683 [==============================] - 1s 880us/sample - loss: 0.3463 - accuracy: 0.8975 - val_loss: 0.5718 - val_accuracy: 0.8121
+Epoch 13/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.3187 - accuracy: 0.9047
683/683 [==============================] - 1s 876us/sample - loss: 0.3211 - accuracy: 0.9063 - val_loss: 0.5532 - val_accuracy: 0.8205
+Epoch 14/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.2591 - accuracy: 0.9500
683/683 [==============================] - 1s 877us/sample - loss: 0.2594 - accuracy: 0.9488 - val_loss: 0.5233 - val_accuracy: 0.8300
+Epoch 15/30
+
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683/683 [==============================] - 1s 877us/sample - loss: 0.2758 - accuracy: 0.9107 - val_loss: 0.5115 - val_accuracy: 0.8361
+Epoch 16/30
+
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683/683 [==============================] - 1s 870us/sample - loss: 0.2316 - accuracy: 0.9429 - val_loss: 0.4839 - val_accuracy: 0.8493
+Epoch 17/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.2252 - accuracy: 0.9422
683/683 [==============================] - 1s 870us/sample - loss: 0.2192 - accuracy: 0.9458 - val_loss: 0.4748 - val_accuracy: 0.8493
+Epoch 18/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.2051 - accuracy: 0.9563
683/683 [==============================] - 1s 909us/sample - loss: 0.2029 - accuracy: 0.9575 - val_loss: 0.4589 - val_accuracy: 0.8554
+Epoch 19/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.1749 - accuracy: 0.9641
683/683 [==============================] - 1s 883us/sample - loss: 0.1719 - accuracy: 0.9649 - val_loss: 0.4372 - val_accuracy: 0.8648
+Epoch 20/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.1489 - accuracy: 0.9688
683/683 [==============================] - 1s 884us/sample - loss: 0.1494 - accuracy: 0.9693 - val_loss: 0.4267 - val_accuracy: 0.8686
+Epoch 21/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.1494 - accuracy: 0.9672
683/683 [==============================] - 1s 877us/sample - loss: 0.1493 - accuracy: 0.9678 - val_loss: 0.4218 - val_accuracy: 0.8648
+Epoch 22/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.1391 - accuracy: 0.9688
683/683 [==============================] - 1s 874us/sample - loss: 0.1395 - accuracy: 0.9663 - val_loss: 0.4032 - val_accuracy: 0.8775
+Epoch 23/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.1245 - accuracy: 0.9797
683/683 [==============================] - 1s 872us/sample - loss: 0.1229 - accuracy: 0.9780 - val_loss: 0.4051 - val_accuracy: 0.8742
+Epoch 24/30
+
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683/683 [==============================] - 1s 873us/sample - loss: 0.1165 - accuracy: 0.9766 - val_loss: 0.3845 - val_accuracy: 0.8837
+Epoch 25/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.1190 - accuracy: 0.9719
683/683 [==============================] - 1s 875us/sample - loss: 0.1241 - accuracy: 0.9678 - val_loss: 0.3887 - val_accuracy: 0.8756
+Epoch 26/30
+
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683/683 [==============================] - 1s 864us/sample - loss: 0.1138 - accuracy: 0.9766 - val_loss: 0.3748 - val_accuracy: 0.8865
+Epoch 27/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.0990 - accuracy: 0.9797
683/683 [==============================] - 1s 873us/sample - loss: 0.1010 - accuracy: 0.9795 - val_loss: 0.3605 - val_accuracy: 0.8902
+Epoch 28/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.1078 - accuracy: 0.9859
683/683 [==============================] - 1s 870us/sample - loss: 0.1049 - accuracy: 0.9854 - val_loss: 0.3621 - val_accuracy: 0.8874
+Epoch 29/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.0917 - accuracy: 0.9766
683/683 [==============================] - 1s 867us/sample - loss: 0.0925 - accuracy: 0.9766 - val_loss: 0.3526 - val_accuracy: 0.8902
+Epoch 30/30
+
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640/683 [===========================>..] - ETA: 0s - loss: 0.0993 - accuracy: 0.9766
683/683 [==============================] - 1s 871us/sample - loss: 0.0970 - accuracy: 0.9766 - val_loss: 0.3682 - val_accuracy: 0.8860
+Train on 756 samples, validate on 2358 samples
+Epoch 1/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 3.9404 - accuracy: 0.2266
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640/756 [========================>.....] - ETA: 0s - loss: 3.2239 - accuracy: 0.2328
756/756 [==============================] - 1s 2ms/sample - loss: 3.1078 - accuracy: 0.2381 - val_loss: 2.1039 - val_accuracy: 0.2383
+Epoch 2/30
+
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640/756 [========================>.....] - ETA: 0s - loss: 2.1816 - accuracy: 0.2625
756/756 [==============================] - 1s 890us/sample - loss: 2.1061 - accuracy: 0.2870 - val_loss: 1.5690 - val_accuracy: 0.4037
+Epoch 3/30
+
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640/756 [========================>.....] - ETA: 0s - loss: 1.5021 - accuracy: 0.4500
756/756 [==============================] - 1s 881us/sample - loss: 1.4505 - accuracy: 0.4656 - val_loss: 1.3096 - val_accuracy: 0.4869
+Epoch 4/30
+
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640/756 [========================>.....] - ETA: 0s - loss: 1.0475 - accuracy: 0.5859
756/756 [==============================] - 1s 902us/sample - loss: 1.0334 - accuracy: 0.5979 - val_loss: 1.1303 - val_accuracy: 0.5640
+Epoch 5/30
+
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640/756 [========================>.....] - ETA: 0s - loss: 0.8917 - accuracy: 0.6344
756/756 [==============================] - 1s 875us/sample - loss: 0.8778 - accuracy: 0.6429 - val_loss: 1.0176 - val_accuracy: 0.6234
+Epoch 6/30
+
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576/756 [=====================>........] - ETA: 0s - loss: 0.6597 - accuracy: 0.7656
756/756 [==============================] - 1s 952us/sample - loss: 0.6539 - accuracy: 0.7646 - val_loss: 0.9308 - val_accuracy: 0.6620
+Epoch 7/30
+
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640/756 [========================>.....] - ETA: 0s - loss: 0.5764 - accuracy: 0.8156
756/756 [==============================] - 1s 875us/sample - loss: 0.5657 - accuracy: 0.8228 - val_loss: 0.8635 - val_accuracy: 0.6976
+Epoch 8/30
+
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640/756 [========================>.....] - ETA: 0s - loss: 0.5413 - accuracy: 0.8203
756/756 [==============================] - 1s 878us/sample - loss: 0.5247 - accuracy: 0.8267 - val_loss: 0.8305 - val_accuracy: 0.7044
+Epoch 9/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.4014 - accuracy: 0.8945
448/756 [================>.............] - ETA: 0s - loss: 0.3982 - accuracy: 0.8996
640/756 [========================>.....] - ETA: 0s - loss: 0.3944 - accuracy: 0.8953
756/756 [==============================] - 1s 871us/sample - loss: 0.4174 - accuracy: 0.8836 - val_loss: 0.7716 - val_accuracy: 0.7303
+Epoch 10/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.4000 - accuracy: 0.8828
448/756 [================>.............] - ETA: 0s - loss: 0.3990 - accuracy: 0.8750
640/756 [========================>.....] - ETA: 0s - loss: 0.3925 - accuracy: 0.8766
756/756 [==============================] - 1s 872us/sample - loss: 0.3848 - accuracy: 0.8783 - val_loss: 0.7322 - val_accuracy: 0.7545
+Epoch 11/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.3456 - accuracy: 0.9141
448/756 [================>.............] - ETA: 0s - loss: 0.3259 - accuracy: 0.9107
640/756 [========================>.....] - ETA: 0s - loss: 0.3185 - accuracy: 0.9172
756/756 [==============================] - 1s 862us/sample - loss: 0.3173 - accuracy: 0.9153 - val_loss: 0.7076 - val_accuracy: 0.7583
+Epoch 12/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.3379 - accuracy: 0.9023
448/756 [================>.............] - ETA: 0s - loss: 0.3347 - accuracy: 0.9040
640/756 [========================>.....] - ETA: 0s - loss: 0.3094 - accuracy: 0.9062
756/756 [==============================] - 1s 873us/sample - loss: 0.3131 - accuracy: 0.9021 - val_loss: 0.6679 - val_accuracy: 0.7782
+Epoch 13/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.3028 - accuracy: 0.9062
448/756 [================>.............] - ETA: 0s - loss: 0.2853 - accuracy: 0.9219
640/756 [========================>.....] - ETA: 0s - loss: 0.2882 - accuracy: 0.9234
756/756 [==============================] - 1s 868us/sample - loss: 0.2859 - accuracy: 0.9233 - val_loss: 0.6344 - val_accuracy: 0.7875
+Epoch 14/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.2638 - accuracy: 0.9219
448/756 [================>.............] - ETA: 0s - loss: 0.2712 - accuracy: 0.9196
640/756 [========================>.....] - ETA: 0s - loss: 0.2596 - accuracy: 0.9297
756/756 [==============================] - 1s 885us/sample - loss: 0.2495 - accuracy: 0.9352 - val_loss: 0.6224 - val_accuracy: 0.7956
+Epoch 15/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.2272 - accuracy: 0.9492
448/756 [================>.............] - ETA: 0s - loss: 0.2219 - accuracy: 0.9487
640/756 [========================>.....] - ETA: 0s - loss: 0.2203 - accuracy: 0.9469
756/756 [==============================] - 1s 873us/sample - loss: 0.2159 - accuracy: 0.9458 - val_loss: 0.6020 - val_accuracy: 0.8007
+Epoch 16/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.1954 - accuracy: 0.9570
448/756 [================>.............] - ETA: 0s - loss: 0.2005 - accuracy: 0.9554
640/756 [========================>.....] - ETA: 0s - loss: 0.2044 - accuracy: 0.9531
756/756 [==============================] - 1s 870us/sample - loss: 0.1989 - accuracy: 0.9550 - val_loss: 0.5926 - val_accuracy: 0.8053
+Epoch 17/30
+
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448/756 [================>.............] - ETA: 0s - loss: 0.1842 - accuracy: 0.9643
640/756 [========================>.....] - ETA: 0s - loss: 0.1888 - accuracy: 0.9609
756/756 [==============================] - 1s 861us/sample - loss: 0.1937 - accuracy: 0.9577 - val_loss: 0.5751 - val_accuracy: 0.8087
+Epoch 18/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.1665 - accuracy: 0.9727
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640/756 [========================>.....] - ETA: 0s - loss: 0.1860 - accuracy: 0.9531
756/756 [==============================] - 1s 875us/sample - loss: 0.1888 - accuracy: 0.9537 - val_loss: 0.5724 - val_accuracy: 0.8024
+Epoch 19/30
+
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640/756 [========================>.....] - ETA: 0s - loss: 0.1793 - accuracy: 0.9438
756/756 [==============================] - 1s 868us/sample - loss: 0.1791 - accuracy: 0.9471 - val_loss: 0.5608 - val_accuracy: 0.8121
+Epoch 20/30
+
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640/756 [========================>.....] - ETA: 0s - loss: 0.1484 - accuracy: 0.9594
756/756 [==============================] - 1s 865us/sample - loss: 0.1600 - accuracy: 0.9563 - val_loss: 0.5365 - val_accuracy: 0.8215
+Epoch 21/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.1258 - accuracy: 0.9844
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640/756 [========================>.....] - ETA: 0s - loss: 0.1410 - accuracy: 0.9672
756/756 [==============================] - 1s 864us/sample - loss: 0.1474 - accuracy: 0.9643 - val_loss: 0.5219 - val_accuracy: 0.8236
+Epoch 22/30
+
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192/756 [======>.......................] - ETA: 0s - loss: 0.1317 - accuracy: 0.9740
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576/756 [=====================>........] - ETA: 0s - loss: 0.1290 - accuracy: 0.9792
756/756 [==============================] - 1s 896us/sample - loss: 0.1337 - accuracy: 0.9749 - val_loss: 0.5295 - val_accuracy: 0.8206
+Epoch 23/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.0798 - accuracy: 0.9922
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640/756 [========================>.....] - ETA: 0s - loss: 0.1140 - accuracy: 0.9766
756/756 [==============================] - 1s 869us/sample - loss: 0.1159 - accuracy: 0.9735 - val_loss: 0.5189 - val_accuracy: 0.8236
+Epoch 24/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.1089 - accuracy: 0.9727
448/756 [================>.............] - ETA: 0s - loss: 0.0981 - accuracy: 0.9777
640/756 [========================>.....] - ETA: 0s - loss: 0.1062 - accuracy: 0.9750
756/756 [==============================] - 1s 871us/sample - loss: 0.1048 - accuracy: 0.9762 - val_loss: 0.5105 - val_accuracy: 0.8240
+Epoch 25/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.1178 - accuracy: 0.9727
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640/756 [========================>.....] - ETA: 0s - loss: 0.1175 - accuracy: 0.9719
756/756 [==============================] - 1s 934us/sample - loss: 0.1102 - accuracy: 0.9749 - val_loss: 0.5067 - val_accuracy: 0.8287
+Epoch 26/30
+
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640/756 [========================>.....] - ETA: 0s - loss: 0.1015 - accuracy: 0.9781
756/756 [==============================] - 1s 873us/sample - loss: 0.0988 - accuracy: 0.9788 - val_loss: 0.4901 - val_accuracy: 0.8367
+Epoch 27/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.0829 - accuracy: 0.9805
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640/756 [========================>.....] - ETA: 0s - loss: 0.0889 - accuracy: 0.9797
756/756 [==============================] - 1s 874us/sample - loss: 0.0896 - accuracy: 0.9788 - val_loss: 0.4939 - val_accuracy: 0.8312
+Epoch 28/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.0875 - accuracy: 0.9883
448/756 [================>.............] - ETA: 0s - loss: 0.0835 - accuracy: 0.9911
640/756 [========================>.....] - ETA: 0s - loss: 0.0919 - accuracy: 0.9875
756/756 [==============================] - 1s 870us/sample - loss: 0.0895 - accuracy: 0.9881 - val_loss: 0.4838 - val_accuracy: 0.8350
+Epoch 29/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.0742 - accuracy: 0.9922
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640/756 [========================>.....] - ETA: 0s - loss: 0.0795 - accuracy: 0.9891
756/756 [==============================] - 1s 880us/sample - loss: 0.0785 - accuracy: 0.9894 - val_loss: 0.4702 - val_accuracy: 0.8405
+Epoch 30/30
+
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256/756 [=========>....................] - ETA: 0s - loss: 0.0712 - accuracy: 0.9883
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640/756 [========================>.....] - ETA: 0s - loss: 0.0823 - accuracy: 0.9859
756/756 [==============================] - 1s 885us/sample - loss: 0.0798 - accuracy: 0.9868 - val_loss: 0.4729 - val_accuracy: 0.8316
+Using TensorFlow backend.