本文整理匯總了Python中tensorflow.python.keras.layers.Reshape方法的典型用法代碼示例。如果您正苦於以下問題：Python layers.Reshape方法的具體用法？Python layers.Reshape怎麽用？Python layers.Reshape使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.python.keras.layers的用法示例。
在下文中一共展示了layers.Reshape方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: GCN
# 需要導入模塊: from tensorflow.python.keras import layers [as 別名]
# 或者: from tensorflow.python.keras.layers import Reshape [as 別名]
def GCN(adj_dim,feature_dim,n_hidden, num_class, num_layers=2,activation=tf.nn.relu,dropout_rate=0.5, l2_reg=0, feature_less=True, ):
Adj = Input(shape=(None,), sparse=True)
if feature_less:
X_in = Input(shape=(1,), )
emb = Embedding(adj_dim, feature_dim,
embeddings_initializer=Identity(1.0), trainable=False)
X_emb = emb(X_in)
h = Reshape([X_emb.shape[-1]])(X_emb)
else:
X_in = Input(shape=(feature_dim,), )
h = X_in
for i in range(num_layers):
if i == num_layers - 1:
activation = tf.nn.softmax
n_hidden = num_class
h = GraphConvolution(n_hidden, activation=activation, dropout_rate=dropout_rate, l2_reg=l2_reg)([h,Adj])
output = h
model = Model(inputs=[X_in,Adj], outputs=output)
return model 示例2: __init__
# 需要導入模塊: from tensorflow.python.keras import layers [as 別名]
# 或者: from tensorflow.python.keras.layers import Reshape [as 別名]
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))