def get_model(batch_size, window_size):
model = Sequential()
model.add(
LSTM(64,
batch_input_shape=(batch_size, window_size, 3),
return_sequences=False,
stateful=False))
model.add(Activation('relu'))
model.Add(Dropout(0.25))
model.add(Dense(6))
model.add(Activation('softmax'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
filename_mlp = 'allFeatures.csv'
dataframe_mlp = read_csv(filename_mlp)
array_mlp = dataframe_mlp.values
Y_mlp = array_mlp[:, 0:71]
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y_mlp,
stratify=Y_mlp,
test_size=0.5,
random_state=0)
X_train_extracted = X_train[:, indices[0:1000]]
X_test_extracted = X_test[:, indices[0:1000]]
model = Sequential()
model.Add(
Dense(500, kernel_initializer='normal', activation='relu', input_dim=1000))
model.add(Dropout(0.5))
model.Add(Dense(250, kernel_initializer='normal', activation='relu'))
model.add(Dropout(0.5))
model.Add(Dense(125, kernel_initializer='normal', activation='relu'))
model.add(Dropout(0.5))
model.Add(Dense(71, kernel_initializer='normal', activation='sigmoid'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X_train_extracted, Y_train, epochs=50, batch_size=100)
scores = model.evaluate(X_test_extracted, Y_test)
