本文整理汇总了Python中tensorflow.keras.layers.Permute方法的典型用法代码示例。如果您正苦于以下问题:Python layers.Permute方法的具体用法?Python layers.Permute怎么用?Python layers.Permute使用的例子?那么, 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.keras.layers
的用法示例。
在下文中一共展示了layers.Permute方法的5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: call
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Permute [as 别名]
def call(self, inputs): # (B, S, H)
# Expand weights to include batch size through implicit broadcasting
W1, W2 = self.W1[None, :, :], self.W2[None, :, :]
hidden_states_transposed = Permute(dims=(2, 1))(inputs) # (B, H, S)
attention_score = tf.matmul(W1, hidden_states_transposed) # (B, size, S)
attention_score = Activation('tanh')(attention_score) # (B, size, S)
attention_weights = tf.matmul(W2, attention_score) # (B, num_hops, S)
attention_weights = Activation('softmax')(attention_weights) # (B, num_hops, S)
embedding_matrix = tf.matmul(attention_weights, inputs) # (B, num_hops, H)
embedding_matrix_flattened = Flatten()(embedding_matrix) # (B, num_hops*H)
if self.use_penalization:
attention_weights_transposed = Permute(dims=(2, 1))(attention_weights) # (B, S, num_hops)
product = tf.matmul(attention_weights, attention_weights_transposed) # (B, num_hops, num_hops)
identity = tf.eye(self.num_hops, batch_shape=(inputs.shape[0],)) # (B, num_hops, num_hops)
frobenius_norm = tf.sqrt(tf.reduce_sum(tf.square(product - identity))) # distance
self.add_loss(self.penalty_coefficient * frobenius_norm) # loss
if self.model_api == 'functional':
return embedding_matrix_flattened, attention_weights
elif self.model_api == 'sequential':
return embedding_matrix_flattened
示例2: squeeze_excite_block
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Permute [as 别名]
def squeeze_excite_block(input_tensor, ratio=16):
""" Create a channel-wise squeeze-excite block
Args:
input_tensor: input Keras tensor
ratio: number of output filters
Returns: a Keras tensor
References
- [Squeeze and Excitation Networks](https://arxiv.org/abs/1709.01507)
"""
init = input_tensor
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
filters = _tensor_shape(init)[channel_axis]
se_shape = (1, 1, filters)
se = GlobalAveragePooling2D()(init)
se = Reshape(se_shape)(se)
se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
if K.image_data_format() == 'channels_first':
se = Permute((3, 1, 2))(se)
x = multiply([init, se])
return x
示例3: channel_squeeze_excite_block
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Permute [as 别名]
def channel_squeeze_excite_block(input, ratio=0.25):
init = input
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
filters = init._keras_shape[channel_axis]
cse_shape = (1, 1, filters)
cse = layers.GlobalAveragePooling2D()(init)
cse = layers.Reshape(cse_shape)(cse)
ratio_filters = int(np.round(filters * ratio))
if ratio_filters < 1:
ratio_filters += 1
cse = layers.Conv2D(
ratio_filters,
(1, 1),
padding="same",
activation="relu",
kernel_initializer="he_normal",
use_bias=False,
)(cse)
cse = layers.BatchNormalization()(cse)
cse = layers.Conv2D(
filters,
(1, 1),
activation="sigmoid",
kernel_initializer="he_normal",
use_bias=False,
)(cse)
if K.image_data_format() == "channels_first":
cse = layers.Permute((3, 1, 2))(cse)
cse = layers.Multiply()([init, cse])
return cse
示例4: squeeze_excite_block
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Permute [as 别名]
def squeeze_excite_block(input, ratio=16):
''' Create a channel-wise squeeze-excite block
Args:
input: input tensor
filters: number of output filters
Returns: a keras tensor
References
- [Squeeze and Excitation Networks](https://arxiv.org/abs/1709.01507)
'''
init = input
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
filters = init._keras_shape[channel_axis]
se_shape = (1, 1, filters)
se = GlobalAveragePooling2D()(init)
se = Reshape(se_shape)(se)
se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
if K.image_data_format() == 'channels_first':
se = Permute((3, 1, 2))(se)
x = multiply([init, se])
return x
示例5: build_and_load_model
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Permute [as 别名]
def build_and_load_model(model_capacity):
"""
Build the CNN model and load the weights
Parameters
----------
model_capacity : 'tiny', 'small', 'medium', 'large', or 'full'
String specifying the model capacity, which determines the model's
capacity multiplier to 4 (tiny), 8 (small), 16 (medium), 24 (large),
or 32 (full). 'full' uses the model size specified in the paper,
and the others use a reduced number of filters in each convolutional
layer, resulting in a smaller model that is faster to evaluate at the
cost of slightly reduced pitch estimation accuracy.
Returns
-------
model : tensorflow.keras.models.Model
The pre-trained keras model loaded in memory
"""
from tensorflow.keras.layers import Input, Reshape, Conv2D, BatchNormalization
from tensorflow.keras.layers import MaxPool2D, Dropout, Permute, Flatten, Dense
from tensorflow.keras.models import Model
if models[model_capacity] is None:
capacity_multiplier = {
'tiny': 4, 'small': 8, 'medium': 16, 'large': 24, 'full': 32
}[model_capacity]
layers = [1, 2, 3, 4, 5, 6]
filters = [n * capacity_multiplier for n in [32, 4, 4, 4, 8, 16]]
widths = [512, 64, 64, 64, 64, 64]
strides = [(4, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1)]
x = Input(shape=(1024,), name='input', dtype='float32')
y = Reshape(target_shape=(1024, 1, 1), name='input-reshape')(x)
for l, f, w, s in zip(layers, filters, widths, strides):
y = Conv2D(f, (w, 1), strides=s, padding='same',
activation='relu', name="conv%d" % l)(y)
y = BatchNormalization(name="conv%d-BN" % l)(y)
y = MaxPool2D(pool_size=(2, 1), strides=None, padding='valid',
name="conv%d-maxpool" % l)(y)
y = Dropout(0.25, name="conv%d-dropout" % l)(y)
y = Permute((2, 1, 3), name="transpose")(y)
y = Flatten(name="flatten")(y)
y = Dense(360, activation='sigmoid', name="classifier")(y)
model = Model(inputs=x, outputs=y)
package_dir = os.path.dirname(os.path.realpath(__file__))
filename = "model-{}.h5".format(model_capacity)
model.load_weights(os.path.join(package_dir, filename))
model.compile('adam', 'binary_crossentropy')
models[model_capacity] = model
return models[model_capacity]