10 examples of 'tf keras layers flatten' in Python

Every line of 'tf keras layers flatten' code snippets is scanned for vulnerabilities by our powerful machine learning engine that combs millions of open source libraries, ensuring your Python code is secure.

All examples are scanned by Snyk Code

By copying the Snyk Code Snippets you agree to
this disclaimer
79def flatten(value, drop_mask=False):
80 if drop_mask:
81 value = DropMask()(value)
82 return Flatten()(value)
Important

Use secure code every time

Secure your code as it's written. Use Snyk Code to scan source code in minutes – no build needed – and fix issues immediately. Enable Snyk Code

391@tf_export('layers.flatten')
392def flatten(inputs, name=None):
393 """Flattens an input tensor while preserving the batch axis (axis 0).
394
395 Arguments:
396 inputs: Tensor input.
397 name: The name of the layer (string).
398
399 Returns:
400 Reshaped tensor.
401
402 Examples:
403
404
x = tf.placeholder(shape=(None, 4, 4), dtype='float32')
y = flatten(x)
# now `y` has shape `(None, 16)`

x = tf.placeholder(shape=(None, 3, None), dtype='float32')
y = flatten(x)
# now `y` has shape `(None, None)`
"""
layer = Flatten(name=name)
return layer.apply(inputs)
195def pass_forward(self, inputs, train_mode = True, **kwargs):
196 self.prev_shape = inputs.shape
197 return np.reshape(inputs, (inputs.shape[0], -1))
166def instantiate_flatten(self, node, tensor, params):
167 return slim.flatten(tensor, **params)
386def build(self, hp, inputs=None):
387 inputs = nest.flatten(inputs)
388 utils.validate_num_inputs(inputs, 1)
389 input_node = inputs[0]
390 if len(input_node.shape) > 2:
391 return tf.keras.layers.Flatten()(input_node)
392 return input_node
57def flatten_layer(layer):
58 # Get the shape of the input layer
59 layer_shape = layer.get_shape()
60
61 # NB: The shape of the input layer is assumed to be:
62 # layer_shape = [num_images, img_height, img_width, num_channels]
63
64 # The number of features is: img_height * img_width * num_channels
65 num_features = layer_shape[1:4].num_elements()
66
67 # Reshape the layer to [num_images, num_features]
68 # NB: We just set the size of the second dimension
69 # to num_features and the size of the first dimension to -1
70 # which means the size in that dimension is calculated
71 # so the total size of the tensor is unchanged from the reshaping
72 layer_flat = tf.reshape(layer, [-1, num_features])
73
74 # NB: The shape of the flattened layer is now:
75 # [num_images, img_height * img_width * num_channels]
76
77 return layer_flat, num_features
66def flatten_layer(layer):
67 '''
68 @param layer: the conv layer
69 '''
70 layer_shape = layer.get_shape() # 获取形状(layer_shape == [num_images, img_height, img_width, num_channels])
71 num_features = layer_shape[1:4].num_elements() # [1:4] 是最后3个维度,就是展开的长度
72 layer_flat = tf.reshape(layer, [-1, num_features]) # 展开
73 return layer_flat, num_features
96def flatten(x: tf.Tensor):
97 c = x.shape[-1]
98 b = tf.shape(x)[0]
99 return tf.reshape(x, [b, -1, c])
125def flatten(inputs):
126 return tf.expand_dims(common_layers.flatten4d3d(inputs), axis=2)
26def forward(self, input, *args, **kwargs):
27 self.last_input_shape = input.shape
28
29 # to_flatten = np.prod(self.last_input_shape[self.outdim-1:])
30 # flattened_shape = input.shape[:self.outdim-1] + (to_flatten, )
31 flattened_shape = input.shape[:self.outdim - 1] + (-1,)
32 return np.reshape(input, flattened_shape)

Related snippets