本文整理汇总了Python中tensorflow.python.keras.optimizers.Adam方法的典型用法代码示例。如果您正苦于以下问题：Python optimizers.Adam方法的具体用法？Python optimizers.Adam怎么用？Python optimizers.Adam使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.keras.optimizers的用法示例。
在下文中一共展示了optimizers.Adam方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from tensorflow.python.keras import optimizers [as 别名]
# 或者: from tensorflow.python.keras.optimizers import Adam [as 别名]
def __init__(self, game, encoder):
"""
NNet model, copied from Othello NNet, with reduced fully connected layers fc1 and fc2 and reduced nnet_args.num_channels
:param game: game configuration
:param encoder: Encoder, used to encode game boards
"""
from rts.src.config_class import CONFIG
# game params
self.board_x, self.board_y, num_encoders = game.getBoardSize()
self.action_size = game.getActionSize()
"""
num_encoders = CONFIG.nnet_args.encoder.num_encoders
"""
num_encoders = encoder.num_encoders
# Neural Net
self.input_boards = Input(shape=(self.board_x, self.board_y, num_encoders)) # s: batch_size x board_x x board_y x num_encoders
x_image = Reshape((self.board_x, self.board_y, num_encoders))(self.input_boards) # batch_size x board_x x board_y x num_encoders
h_conv1 = Activation('relu')(BatchNormalization(axis=3)(Conv2D(CONFIG.nnet_args.num_channels, 3, padding='same', use_bias=False)(x_image))) # batch_size x board_x x board_y x num_channels
h_conv2 = Activation('relu')(BatchNormalization(axis=3)(Conv2D(CONFIG.nnet_args.num_channels, 3, padding='same', use_bias=False)(h_conv1))) # batch_size x board_x x board_y x num_channels
h_conv3 = Activation('relu')(BatchNormalization(axis=3)(Conv2D(CONFIG.nnet_args.num_channels, 3, padding='valid', use_bias=False)(h_conv2))) # batch_size x (board_x-2) x (board_y-2) x num_channels
h_conv4 = Activation('relu')(BatchNormalization(axis=3)(Conv2D(CONFIG.nnet_args.num_channels, 3, padding='valid', use_bias=False)(h_conv3))) # batch_size x (board_x-4) x (board_y-4) x num_channels
h_conv4_flat = Flatten()(h_conv4)
s_fc1 = Dropout(CONFIG.nnet_args.dropout)(Activation('relu')(BatchNormalization(axis=1)(Dense(256, use_bias=False)(h_conv4_flat)))) # batch_size x 1024
s_fc2 = Dropout(CONFIG.nnet_args.dropout)(Activation('relu')(BatchNormalization(axis=1)(Dense(128, use_bias=False)(s_fc1)))) # batch_size x 1024
self.pi = Dense(self.action_size, activation='softmax', name='pi')(s_fc2) # batch_size x self.action_size
self.v = Dense(1, activation='tanh', name='v')(s_fc2) # batch_size x 1
self.model = Model(inputs=self.input_boards, outputs=[self.pi, self.v])
self.model.compile(loss=['categorical_crossentropy', 'mean_squared_error'], optimizer=Adam(CONFIG.nnet_args.lr))