Train neural network on videos

train_videos.py

from keras.models import Sequential, load_model
from keras.layers import Dense, Activation, Dropout
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score

MODEL_PATH='model.h5'
EPOCHS = 10
HIDDEN_SIZE = 16

model = Sequential()
model.add(Dense(HIDDEN_SIZE, input_shape=(X.shape[1],)))
model.add(Dense(HIDDEN_SIZE))
model.add(Dropout(0.2))
model.add(Dense(len(CLASSES), activation='softmax'))

model.compile(loss='categorical_crossentropy', 
              optimizer='rmsprop', 
              metrics=['accuracy'])

x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=42)

model.fit(x_train, y_train,
          batch_size=10, epochs=EPOCHS,
          validation_split=0.1)

model.save(MODEL_PATH)
y_true = [np.argmax(y) for y in y_test]
y_pred = [np.argmax(pred) for pred in model.predict(x_test)]
score = f1_score(y_true, y_pred)
print('F1:', score)

# Use this to load the model
model = load_model(MODEL_PATH)