chore: add to the readme and restructure

Neural_Network_Analysis.py

@@ -9,7 +9,7 @@ import pandas as pd
 import matplotlib.pyplot as plt
 
 # path to json file that stores MFCCs and subject labels for each processed sample
-DATA_PATH = str(Path.cwd()) + "/mfcc_data.json"
+DATA_PATH_MFCC = str(Path.cwd()) + "/mfcc_data.json"
 
 def load_data_from_json(data_path): 
 
@@ -57,29 +57,16 @@ def plot_history(history):
     plt.show()
 
 
-def prepare_datasets(test_size=0.25, validation_size=0.2):
+def prepare_datasets_percentsplit(X, y, shuffle_vars, validation_size=0.2, test_size=0.25,):
-    """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, validation and test split
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, shuffle=shuffle_vars)
-    X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)
+    X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size, shuffle=shuffle_vars)
 
     return X_train, X_validation, X_test, y_train, y_validation, y_test
 
 
-def build_model(input_shape, nr_classes=5):
+def RNN_LSTM(input_shape, nr_classes=5):
     """Generates RNN-LSTM model
     :param input_shape (tuple): Shape of input set
     :return model: RNN-LSTM model
@@ -101,29 +88,39 @@ def build_model(input_shape, nr_classes=5):
 
     return model
 
+def train(model, batch_size, epochs, X_train, X_validation, y_train, y_validation):
 
-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)
-
-    print(X_train.shape[1], X_train.shape[2])
-    # create network
-    
-    input_shape = (X_train.shape[1], X_train.shape[2])  # (~2800), 1, 208
-    model = build_model(input_shape)
-
-    # compile model
     optimiser = keras.optimizers.Adam(learning_rate=0.0001)
     model.compile(optimizer=optimiser,
                   loss='sparse_categorical_crossentropy',
                   metrics=['accuracy'])
 
+    history = model.fit(X_train, 
+                        y_train, 
+                        validation_data=(X_validation, y_validation), 
+                        batch_size=batch_size, 
+                        epochs=epochs)
+    return history
+
+if __name__ == "__main__":
+
+    # Load data
+    X, y = load_data_from_json(DATA_PATH_MFCC)
+
+    # Get prepared data: train, validation, and test
+    X_train, X_validation, X_test, y_train, y_validation, y_test = prepare_datasets_percentsplit(X, y,
+                                                                                                validation_size=0.2, 
+                                                                                                test_size=0.25,  
+                                                                                                shuffle_vars=True)
+    #print(X_train.shape[1], X_train.shape[2])
+
+    # Make model
+    model = RNN_LSTM(input_shape=(1, 208))
     model.summary()
-
+    
-    # train model
+    # Train network
-    history = model.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=64, epochs=30)
+    history = train(model, X_train, X_validation, y_train, y_validation, batch_size=64, epochs=30)
-
+    
     # plot accuracy/error for training and validation
     plot_history(history)
 

README.md

@@ -17,9 +17,9 @@ Scripts to handle CSV files composed by 2 * 8 EMG sensors(left & right) devided
 
 #### Credits for insporational code
 
-* Kapre - Keunwoochoi
+* Kapre: Keunwoochoi
 * Audio-Classification: seth814
-* DeepLearningForAudioWithPyhton - musikalkemist
+* DeepLearningForAudioWithPyhton: musikalkemist
 
 ## Table of Contents
 
@@ -31,7 +31,16 @@ Scripts to handle CSV files composed by 2 * 8 EMG sensors(left & right) devided
 | Neural_Network_Analysis.py | Contains functions to load, build and execute analysis with Neural Networks. Main functions are <br>load_data_from_json(), build_model(), and main() |
 
 
+## How to use it 
 
-
+1. Clone the repo
-
+2. Place the data files in the working directory 
+3. (For now) Add the session filenames in the desired load_data() function 
+4. Assuming NN analysis:
+    1. Create `CSV_handler` object 
+    2. Load data with `load_data(CSV_handler, <datatype>)`
+    3. Create `NN_handler` object with <CSV_handler> as input
+    4. Load MFCC data into the `NN_handler` with `store_mfcc_samples()`
+    5. Run `save_json_mfcc()`
+    6. Run `Neural_Network_Analysis.py`