fix: try to implement the NN correctly

.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/

.idea/Slovakia 2021.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/encodings.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding" addBOMForNewFiles="with NO BOM" />
+</project>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (Slovakia 2021)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Slovakia 2021.iml" filepath="$PROJECT_DIR$/.idea/Slovakia 2021.iml" />
+    </modules>
+  </component>
+</project>

.idea/other.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="PySciProjectComponent">
+    <option name="PY_SCI_VIEW_SUGGESTED" value="true" />
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/python_speech_features" vcs="Git" />
+  </component>
+</project>

Handle_emg_data.py

@@ -610,15 +610,10 @@ class DL_data_handler:
                 test_df_for_bugs(signal, key, i)
 
                 # extract mfcc
-                #n_fft = MFCC_WINDOWSIZE * sample_rate
+
-                #hop_length = MFCC_STEPSIZE * sample_rate
-                #mfcc = librosa.feature.mfcc(signal, sample_rate, n_mfcc=NR_COEFFICIENTS, n_fft=n_fft, hop_length=hop_length)
                 mfcc = mfcc_custom(signal, sample_rate, MFCC_WINDOWSIZE, MFCC_STEPSIZE, NR_COEFFICIENTS, NR_MEL_BINS)
                 mfcc = mfcc.T
-                #print(len(mfcc))
 
-                # store only mfcc feature with expected number of vectors
-                #if len(mfcc) == num_mfcc_vectors_per_segment:
                 data["mfcc"].append(mfcc.tolist())
                 data["labels"].append(key)
                 print("sample:{}".format(i+1))

Neural_Network_Analysis.py

@@ -1,12 +1,14 @@
 import json
 import numpy as np
 from sklearn.model_selection import train_test_split
+import tensorflow.keras as keras
 import tensorflow as tf
-import tf.keras as keras
 from pathlib import Path
+import pandas as pd
+import matplotlib.pyplot as plt
 
-# path to json file that stores MFCCs and genre labels for each processed segment
+# path to json file that stores MFCCs and subject labels for each processed sample
-DATA_PATH = str(Path.cwd()) + "mfcc_data.json"
+DATA_PATH = str(Path.cwd()) + "/mfcc_data.json"
 
 def load_data(data_path): 
 
@@ -16,38 +18,102 @@ def load_data(data_path):
     # convert lists to numpy arrays
     X = np.array(data["mfcc"])
     y = np.array(data["labels"])
+    #X = np.asarray(X).astype('float32')
+    #y = np.asarray(y).astype('float32')
+    #y = tf.expand_dims(y, axis=1)
 
     print("Data succesfully loaded!")
 
-    return  X, y
+    return X, y
+
+def plot_history(history):
+    """Plots accuracy/loss for training/validation set as a function of the epochs
+        :param history: Training history of model
+        :return:
+    """
+
+    fig, axs = plt.subplots(2)
+
+    # create accuracy sublpot
+    axs[0].plot(history.history["accuracy"], label="train accuracy")
+    axs[0].plot(history.history["val_accuracy"], label="test accuracy")
+    axs[0].set_ylabel("Accuracy")
+    axs[0].legend(loc="lower right")
+    axs[0].set_title("Accuracy eval")
+
+    # create error sublpot
+    axs[1].plot(history.history["loss"], label="train error")
+    axs[1].plot(history.history["val_loss"], label="test error")
+    axs[1].set_ylabel("Error")
+    axs[1].set_xlabel("Epoch")
+    axs[1].legend(loc="upper right")
+    axs[1].set_title("Error eval")
+
+    plt.show()
 
 
-if __name__ == "__main__":
+def prepare_datasets(test_size=0.25, validation_size=0.2):
+    """Loads data and splits it into train, validation and test sets.
+    :param test_size (float): Value in [0, 1] indicating percentage of data set to allocate to test split
+    :param validation_size (float): Value in [0, 1] indicating percentage of train set to allocate to validation split
+    :return X_train (ndarray): Input training set
+    :return X_validation (ndarray): Input validation set
+    :return X_test (ndarray): Input test set
+    :return y_train (ndarray): Target training set
+    :return y_validation (ndarray): Target validation set
+    :return y_test (ndarray): Target test set
+    """
 
     # load data
     X, y = load_data(DATA_PATH)
 
-    # create train/test split
+    # create train, validation and test split
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
+    X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)
+
+    return X_train, X_validation, X_test, y_train, y_validation, y_test
+
+
+def build_model(input_shape, nr_classes=5):
+    """Generates RNN-LSTM model
+    :param input_shape (tuple): Shape of input set
+    :return model: RNN-LSTM model
+    """
 
     # build network topology
-    model = keras.Sequential([
+    model = keras.Sequential()
 
-        # input layer
+    # 2 LSTM layers
-        keras.layers.Flatten(input_shape=(X.shape[1], X.shape[2])),
+    model.add(keras.layers.LSTM(64, input_shape=input_shape, return_sequences=True))
+    model.add(keras.layers.LSTM(64))
 
-        # 1st dense layer
+    # dense layer
-        keras.layers.Dense(512, activation='relu'),
+    model.add(keras.layers.Dense(64, activation='relu'))
+    model.add(keras.layers.Dropout(0.3))
 
-        # 2nd dense layer
+    # output layer
-        keras.layers.Dense(256, activation='relu'),
+    model.add(keras.layers.Dense(nr_classes, activation='softmax'))
 
-        # 3rd dense layer
+    return model
-        keras.layers.Dense(64, activation='relu'),
 
-        # output layer
+
-        keras.layers.Dense(10, activation='softmax')
+if __name__ == "__main__":
-    ])
+
+    # get train, validation, test splits
+    X_train, X_validation, X_test, y_train, y_validation, y_test = prepare_datasets(0.25, 0.2)
+
+    # create network
+
+    print(X_train.shape)
+    X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
+    X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
+    #X_validation = np.reshape(X_validation, (X_test.shape[0], 1, X_test.shape[1]))
+    print(X_train.shape)
+    print(X_train.shape[0])
+    print(X_train.shape[1])
+
+    input_shape = (X_train.shape[1], X_train.shape[2])  # 300, 13
+    model = build_model(input_shape)
 
     # compile model
     optimiser = keras.optimizers.Adam(learning_rate=0.0001)
@@ -58,4 +124,12 @@ if __name__ == "__main__":
     model.summary()
 
     # train model
-    history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=32, epochs=50)
+    history = model.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=16, epochs=30)
+
+    # plot accuracy/error for training and validation
+    plot_history(history)
+
+    # evaluate model on test set
+    test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
+    print('\nTest accuracy:', test_acc)
+