chore: import tensorflow and keras

Handle_emg_data.py

@@ -4,13 +4,11 @@ import pandas as pd
 from pathlib import Path
 import numpy as np
 from pandas.core.frame import DataFrame
-from math import floor
 import sys
 sys.path.insert(0, '/Users/Markus/Prosjekter git/Slovakia 2021/python_speech_features/python_speech_features')
 from python_speech_features.python_speech_features import mfcc
 import json
-#import librosa
+
-#from Present_data import get_data
 
 # Global variables for MFCC
 MFCC_STEPSIZE = 0.5     # Seconds

Neural_Network_Analysis.py

@@ -0,0 +1,61 @@
+import json
+import numpy as np
+from sklearn.model_selection import train_test_split
+import tensorflow as tf
+import tf.keras as keras
+from pathlib import Path
+
+# path to json file that stores MFCCs and genre labels for each processed segment
+DATA_PATH = str(Path.cwd()) + "mfcc_data.json"
+
+def load_data(data_path): 
+
+    with open(data_path, "r") as fp:
+        data = json.load(fp)
+
+    # convert lists to numpy arrays
+    X = np.array(data["mfcc"])
+    y = np.array(data["labels"])
+
+    print("Data succesfully loaded!")
+
+    return  X, y
+
+
+if __name__ == "__main__":
+
+    # load data
+    X, y = load_data(DATA_PATH)
+
+    # create train/test split
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
+
+    # build network topology
+    model = keras.Sequential([
+
+        # input layer
+        keras.layers.Flatten(input_shape=(X.shape[1], X.shape[2])),
+
+        # 1st dense layer
+        keras.layers.Dense(512, activation='relu'),
+
+        # 2nd dense layer
+        keras.layers.Dense(256, activation='relu'),
+
+        # 3rd dense layer
+        keras.layers.Dense(64, activation='relu'),
+
+        # output layer
+        keras.layers.Dense(10, activation='softmax')
+    ])
+
+    # compile model
+    optimiser = keras.optimizers.Adam(learning_rate=0.0001)
+    model.compile(optimizer=optimiser,
+                  loss='sparse_categorical_crossentropy',
+                  metrics=['accuracy'])
+
+    model.summary()
+
+    # train model
+    history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=32, epochs=50)