def build_model(self):
model = Sequential()
model.add(Dense(24, input_dim=self.state_size, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.summary()
model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
return model
def test_regularizer(self):
model = ZSequential()
model.add(
ZLayer.Dense(16,
W_regularizer=regularizers.l2(0.001),
activation='relu',
input_shape=(10000, )))
model.summary()
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['acc'])
predictionColumn = 'slotOccupancy'
x = trainDf.drop(columns=[predictionColumn])
inputs = len(x.columns)
y = trainDf[[predictionColumn]]
outputs = len(y.columns)
model = Sequential()
model.add(Dense(output_dim=inputs, activation="relu", input_shape=(inputs, )))
model.add(Dense(output_dim=inputs, activation="relu"))
model.add(Dense(output_dim=outputs))
model.compile(optimizer="adam", loss="mean_squared_error")
model.summary()
print("Created Sequential Model!\n")
xNumpy = x.to_numpy()
yNumpy = y.to_numpy()
# model.fit(x=xNumpy, y=yNumpy, nb_epoch=1, distributed=False)
import tensorflow as tf
weights = np.array(model.get_weights(), dtype=object)
print(weights)
tfModel = tf.keras.models.Sequential()
tfModel.add(
tf.keras.layers.Dense(units=inputs,
