def create_model(self): model = Sequential() model.add(Conv2D(256, (3, 3), input_shape=Resized.shape)) model.add(Activation='relu') model.Dropout(0.2) model.add(Conv2D(256. (3, 3))) model.add(Acitvation='relu') model.Dropout(0.2) model.add(Flatten()) model.add(Dense(64)) model.add(Dense(action_choices, activation='linear')) model.compile(loss='mse', optimizer=Adam(lr=0.01), metrics=['accuracy']) return model
def fit_mlp(X_train, Y_train, X_val, Y_val, neurons, param, verbose=False): """ Three hidden layers and one dropout applied to the first hidden layer """ mdl = Sequential() mdl.add(Dense(neurons, input_dim=param.n_lags, activation='tanh')) mdl.Dropout(param.ratio_dropout) mdl.add(Dense(neurons, activation='tanh')) mdl.add(Dense(param.pre_step)) mdl.compile(loss='mean_squared_error', optimizer='adam') mdl.fit(X_train, Y_train, epochs=nb_epochs, batch_size=nb_batch_size, verbose=verbose, shuffle=False) return mdl
model.add(MaxPool2D(pool_size=(2, 2))) model.add(Conv2D(32, (2, 2), activation='relu')) model.add(MaxPool2D(pool_size=(2, 2))) model.add(Conv2D(64, (2, 2), activation='relu')) model.add(MaxPool2D(pool_size=(2, 2))) model.add(Conv2D(32, (2, 2), activation='relu')) model.add(MaxPool2D(pool_size=(2, 2))) model.add(Conv2D(64, (2, 2), activation='relu')) model.add(MaxPool2D(pool_size=(2, 2))) model.add(Dense(units=1024, activation='relu')) model.Dropout(0.8) model.add(Dense(units=2, activation='softmax')) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.add(Dense(units=1, activation='sigmoid')) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) from keras.preprocessing.image import ImageDataGenerator train_datagen = ImageDataGenerator(rescale=1. / 255, shear_range=0.2, zoom_range=0.2,