10 examples of 'tf keras layers flatten' in Python

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def flatten(value, drop_mask=False):
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    if drop_mask:
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        value = DropMask()(value)
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    return Flatten()(value)

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@tf_export('layers.flatten')
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def flatten(inputs, name=None):
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  """Flattens an input tensor while preserving the batch axis (axis 0).
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  Arguments:
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    inputs: Tensor input.
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    name: The name of the layer (string).
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  Returns:
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    Reshaped tensor.
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  Examples:
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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)

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def pass_forward(self, inputs, train_mode = True, **kwargs):
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    self.prev_shape = inputs.shape
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    return np.reshape(inputs, (inputs.shape[0], -1))

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def instantiate_flatten(self, node, tensor, params):
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    return slim.flatten(tensor, **params)

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def build(self, hp, inputs=None):
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    inputs = nest.flatten(inputs)
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    utils.validate_num_inputs(inputs, 1)
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    input_node = inputs[0]
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    if len(input_node.shape) > 2:
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        return tf.keras.layers.Flatten()(input_node)
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    return input_node

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def flatten_layer(layer):
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    # Get the shape of the input layer
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    layer_shape = layer.get_shape()
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    # NB: The shape of the input layer is assumed to be:
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    # layer_shape = [num_images, img_height, img_width, num_channels]
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    # The number of features is: img_height * img_width * num_channels
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    num_features = layer_shape[1:4].num_elements()
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    # Reshape the layer to [num_images, num_features]
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    # NB: We just set the size of the second dimension
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    # to num_features and the size of the first dimension to -1
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    # which means the size in that dimension is calculated
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    # so the total size of the tensor is unchanged from the reshaping
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    layer_flat = tf.reshape(layer, [-1, num_features])
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    # NB: The shape of the flattened layer is now:
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    # [num_images, img_height * img_width * num_channels]
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    return layer_flat, num_features

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def flatten_layer(layer):
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    '''
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    @param layer: the conv layer
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    '''
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    layer_shape = layer.get_shape() # 获取形状(layer_shape == [num_images, img_height, img_width, num_channels])
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    num_features = layer_shape[1:4].num_elements()  # [1:4] 是最后3个维度，就是展开的长度
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    layer_flat = tf.reshape(layer, [-1, num_features])   # 展开
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    return layer_flat, num_features

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def flatten(x: tf.Tensor):
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  c = x.shape[-1]
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  b = tf.shape(x)[0]
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  return tf.reshape(x, [b, -1, c])

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def flatten(inputs):
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  return tf.expand_dims(common_layers.flatten4d3d(inputs), axis=2)

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def forward(self, input, *args, **kwargs):
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    self.last_input_shape = input.shape
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    # to_flatten = np.prod(self.last_input_shape[self.outdim-1:])
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    # flattened_shape = input.shape[:self.outdim-1] + (to_flatten, )
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    flattened_shape = input.shape[:self.outdim - 1] + (-1,)
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    return np.reshape(input, flattened_shape)