本文整理匯總了Python中keras.layers.Dense.get_shape方法的典型用法代碼示例。如果您正苦於以下問題：Python Dense.get_shape方法的具體用法？Python Dense.get_shape怎麽用？Python Dense.get_shape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keras.layers.Dense的用法示例。
在下文中一共展示了Dense.get_shape方法的4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: __build_network
# 需要導入模塊: from keras.layers import Dense [as 別名]
# 或者: from keras.layers.Dense import get_shape [as 別名]
def __build_network(self):
embedding_layer = Embedding(
self.corpus_size,
EMBEDDING_DIM,
weights=[self.embedding_matrix],
input_length=MAX_TITLE_LENGTH,
trainable=False)
# train a 1D convnet with global maxpooling
sequence_input = Input(shape=(MAX_TITLE_LENGTH, ), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
x = LSTM(
128,
dropout_W=0.2,
dropout_U=0.2,
W_regularizer=regularizers.l2(0.01),
b_regularizer=regularizers.l2(0.01))(embedded_sequences)
x = Dropout(0.5)(x)
preds = Dense(self.class_num, activation='softmax')(x)
print preds.get_shape()
if self.optimizer == 'adam':
self.optimizer = Adam(lr=self.lr)
if self.optimizer == 'rmsprop':
self.optimizer = RMSprop(lr=self.lr)
# rmsprop = RMSprop(lr=self.lr)
self.model = Model(sequence_input, preds)
self.model.compile(
loss='categorical_crossentropy',
optimizer=self.optimizer,
metrics=['acc'])示例2: __build_network
# 需要導入模塊: from keras.layers import Dense [as 別名]
# 或者: from keras.layers.Dense import get_shape [as 別名]
def __build_network(self):
embedding_layer = Embedding(self.corpus_size,
EMBEDDING_DIM,
weights=[self.embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=False)
# train a 1D convnet with global maxpooling
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
# sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
# embedded_sequences = embedding_layer(sequence_input)
x = Convolution1D(128, 5)(embedded_sequences)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling1D(5)(x)
x = Convolution1D(128, 5)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling1D(5)(x)
print "before 256", x.get_shape()
x = Convolution1D(128, 5)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
print "before 35 ", x.get_shape()
x = MaxPooling1D(35)(x)
x = Flatten()(x)
# print x.shape()
x = Dense(128, activation='relu')(x)
print x.get_shape()
x = Dropout(0.5)(x)
print x.get_shape()
preds = Dense(self.class_num, activation='softmax')(x)
print preds.get_shape()
# conv_blocks = []
# for sz in self.filter_sizes:
# conv = Convolution1D(filters=self.num_filters, kernel_size=sz, activation="relu", padding='valid', strides=1)(embedded_sequences)
# conv = MaxPooling1D(pool_size=2)(conv)
# conv = Flatten()(conv)
# conv_blocks.append(conv)
# z = Concatenate()(conv_blocks) if len(conv_blocks) > 1 else conv_blocks[0]
# z = Dropout(rate=0.5)(z)
# z = Dense(units=self.hidden_dims, activation="relu")(z)
# preds = Dense(self.class_num, activation="softmax")(z)
rmsprop = RMSprop(lr=0.001)
self.model = Model(sequence_input, preds)
self.model.compile(loss='categorical_crossentropy', optimizer=rmsprop, metrics=['acc'])示例3: __build_network
# 需要導入模塊: from keras.layers import Dense [as 別名]
# 或者: from keras.layers.Dense import get_shape [as 別名]
def __build_network(self):
embedding_layer = Embedding(self.corpus_size,
EMBEDDING_DIM,
weights=[self.embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=False)
# train a 1D convnet with global maxpooling
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
x = Convolution1D(self.num_filters, 5, activation="relu")(embedded_sequences)
x = MaxPooling1D(5)(x)
x = Convolution1D(self.num_filters, 5, activation="relu")(x)
x = MaxPooling1D(5)(x)
x = LSTM(64, dropout_W=0.2, dropout_U=0.2)(x)
preds = Dense(self.class_num, activation='softmax')(x)
print preds.get_shape()
rmsprop = RMSprop(lr=0.01)
self.model = Model(sequence_input, preds)
self.model.compile(loss='categorical_crossentropy', optimizer=rmsprop, metrics=['acc'])示例4: get_ResNet_classifier
# 需要導入模塊: from keras.layers import Dense [as 別名]
# 或者: from keras.layers.Dense import get_shape [as 別名]
def get_ResNet_classifier():
inputs = Input((CLASSIFY_INPUT_WIDTH, CLASSIFY_INPUT_HEIGHT, CLASSIFY_INPUT_DEPTH, CLASSIFY_INPUT_CHANNEL))
x = conv_bn_relu(inputs, RESNET_INITIAL_FILTERS)
print('base')
print(x.get_shape())
for i in range(RESNET_BLOCKS):
x = bottleneck(x, shrinkage=(i % RESNET_SHRINKAGE_STEPS == 0))
print('top')
x = GlobalMaxPooling3D()(x)
print(x.get_shape())
x = Dense(2, activation='softmax')(x)
print(x.get_shape())
model = Model(inputs=inputs, outputs=x)
model.compile(optimizer=Adam(lr=TRAIN_CLASSIFY_LEARNING_RATE), loss='binary_crossentropy', metrics=['accuracy'])
return model