Python tensorflow.python.ops.nn.conv2d_transpose() Examples

The following are 6 code examples of tensorflow.python.ops.nn.conv2d_transpose(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.ops.nn , or try the search function .
Example #1
Source File: convolutional.py    From lambda-packs with MIT License 4 votes vote down vote up 
def call(self, inputs):
    inputs_shape = array_ops.shape(inputs)
    batch_size = inputs_shape[0]
    if self.data_format == 'channels_first':
      c_axis, h_axis, w_axis = 1, 2, 3
    else:
      c_axis, h_axis, w_axis = 3, 1, 2

    height, width = inputs_shape[h_axis], inputs_shape[w_axis]
    kernel_h, kernel_w = self.kernel_size
    stride_h, stride_w = self.strides

    # Infer the dynamic output shape:
    out_height = utils.deconv_output_length(height,
                                            kernel_h,
                                            self.padding,
                                            stride_h)
    out_width = utils.deconv_output_length(width,
                                           kernel_w,
                                           self.padding,
                                           stride_w)
    if self.data_format == 'channels_first':
      output_shape = (batch_size, self.filters, out_height, out_width)
      strides = (1, 1, stride_h, stride_w)
    else:
      output_shape = (batch_size, out_height, out_width, self.filters)
      strides = (1, stride_h, stride_w, 1)

    output_shape_tensor = array_ops.stack(output_shape)
    outputs = nn.conv2d_transpose(
        inputs,
        self.kernel,
        output_shape_tensor,
        strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, ndim=4))

    # Infer the static output shape:
    out_shape = inputs.get_shape().as_list()
    out_shape[c_axis] = self.filters
    out_shape[h_axis] = utils.deconv_output_length(out_shape[h_axis],
                                                   kernel_h,
                                                   self.padding,
                                                   stride_h)
    out_shape[w_axis] = utils.deconv_output_length(out_shape[w_axis],
                                                   kernel_w,
                                                   self.padding,
                                                   stride_w)
    outputs.set_shape(out_shape)

    if self.bias:
      outputs = nn.bias_add(
          outputs,
          self.bias,
          data_format=utils.convert_data_format(self.data_format, ndim=4))

    if self.activation is not None:
      return self.activation(outputs)
    return outputs
Example #2
Source File: backend.py    From lambda-packs with MIT License 4 votes vote down vote up 
def conv2d_transpose(x,
                     kernel,
                     output_shape,
                     strides=(1, 1),
                     padding='valid',
                     data_format=None):
  """2D deconvolution (i.e.

  transposed convolution).

  Arguments:
      x: Tensor or variable.
      kernel: kernel tensor.
      output_shape: 1D int tensor for the output shape.
      strides: strides tuple.
      padding: string, `"same"` or `"valid"`.
      data_format: `"channels_last"` or `"channels_first"`.
          Whether to use Theano or TensorFlow data format
          for inputs/kernels/ouputs.

  Returns:
      A tensor, result of transposed 2D convolution.

  Raises:
      ValueError: if `data_format` is neither `channels_last` or
      `channels_first`.
  """
  if data_format is None:
    data_format = image_data_format()
  if data_format not in {'channels_first', 'channels_last'}:
    raise ValueError('Unknown data_format ' + str(data_format))
  if isinstance(output_shape, (tuple, list)):
    output_shape = array_ops.stack(output_shape)

  x = _preprocess_conv2d_input(x, data_format)
  output_shape = _preprocess_deconv_output_shape(x, output_shape, data_format)
  padding = _preprocess_padding(padding)
  strides = (1,) + strides + (1,)

  x = nn.conv2d_transpose(x, kernel, output_shape, strides, padding=padding)
  x = _postprocess_conv2d_output(x, data_format)
  return x
Example #3
Source File: convolutional.py    From auto-alt-text-lambda-api with MIT License 4 votes vote down vote up 
def call(self, inputs):
    inputs_shape = array_ops.shape(inputs)
    batch_size = inputs_shape[0]
    if self.data_format == 'channels_first':
      c_axis, h_axis, w_axis = 1, 2, 3
    else:
      c_axis, h_axis, w_axis = 3, 1, 2

    height, width = inputs_shape[h_axis], inputs_shape[w_axis]
    kernel_h, kernel_w = self.kernel_size
    stride_h, stride_w = self.strides

    def get_deconv_dim(dim_size, stride_size, kernel_size, padding):
      if isinstance(dim_size, ops.Tensor):
        dim_size = math_ops.multiply(dim_size, stride_size)
      elif dim_size is not None:
        dim_size *= stride_size

      if padding == 'valid' and dim_size is not None:
        dim_size += max(kernel_size - stride_size, 0)
      return dim_size

    # Infer the dynamic output shape:
    out_height = get_deconv_dim(height, stride_h, kernel_h, self.padding)
    out_width = get_deconv_dim(width, stride_w, kernel_w, self.padding)

    if self.data_format == 'channels_first':
      output_shape = (batch_size, self.filters, out_height, out_width)
      strides = (1, 1, stride_h, stride_w)
    else:
      output_shape = (batch_size, out_height, out_width, self.filters)
      strides = (1, stride_h, stride_w, 1)

    output_shape_tensor = array_ops.stack(output_shape)
    outputs = nn.conv2d_transpose(
        inputs,
        self.kernel,
        output_shape_tensor,
        strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, ndim=4))

    # Infer the static output shape:
    out_shape = inputs.get_shape().as_list()
    out_shape[c_axis] = self.filters
    out_shape[h_axis] = get_deconv_dim(
        out_shape[h_axis], stride_h, kernel_h, self.padding)
    out_shape[w_axis] = get_deconv_dim(
        out_shape[w_axis], stride_w, kernel_w, self.padding)
    outputs.set_shape(out_shape)

    if self.bias:
      outputs = nn.bias_add(
          outputs,
          self.bias,
          data_format=utils.convert_data_format(self.data_format, ndim=4))

    if self.activation is not None:
      return self.activation(outputs)
    return outputs
Example #4
Source File: backend.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 4 votes vote down vote up 
def conv2d_transpose(x,
                     kernel,
                     output_shape,
                     strides=(1, 1),
                     padding='valid',
                     data_format=None):
  """2D deconvolution (i.e.

  transposed convolution).

  Arguments:
      x: Tensor or variable.
      kernel: kernel tensor.
      output_shape: 1D int tensor for the output shape.
      strides: strides tuple.
      padding: string, `"same"` or `"valid"`.
      data_format: `"channels_last"` or `"channels_first"`.
          Whether to use Theano or TensorFlow data format
          for inputs/kernels/outputs.

  Returns:
      A tensor, result of transposed 2D convolution.

  Raises:
      ValueError: if `data_format` is neither `channels_last` or
      `channels_first`.
  """
  if data_format is None:
    data_format = image_data_format()
  if data_format not in {'channels_first', 'channels_last'}:
    raise ValueError('Unknown data_format ' + str(data_format))
  if isinstance(output_shape, (tuple, list)):
    output_shape = array_ops.stack(output_shape)

  x = _preprocess_conv2d_input(x, data_format)
  output_shape = _preprocess_deconv_output_shape(x, output_shape, data_format)
  padding = _preprocess_padding(padding)
  strides = (1,) + strides + (1,)

  x = nn.conv2d_transpose(x, kernel, output_shape, strides, padding=padding)
  x = _postprocess_conv2d_output(x, data_format)
  return x
Example #5
Source File: convolutional.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 4 votes vote down vote up 
def call(self, inputs):
    inputs_shape = array_ops.shape(inputs)
    batch_size = inputs_shape[0]
    if self.data_format == 'channels_first':
      c_axis, h_axis, w_axis = 1, 2, 3
    else:
      c_axis, h_axis, w_axis = 3, 1, 2

    height, width = inputs_shape[h_axis], inputs_shape[w_axis]
    kernel_h, kernel_w = self.kernel_size
    stride_h, stride_w = self.strides

    # Infer the dynamic output shape:
    out_height = utils.deconv_output_length(height,
                                            kernel_h,
                                            self.padding,
                                            stride_h)
    out_width = utils.deconv_output_length(width,
                                           kernel_w,
                                           self.padding,
                                           stride_w)
    if self.data_format == 'channels_first':
      output_shape = (batch_size, self.filters, out_height, out_width)
      strides = (1, 1, stride_h, stride_w)
    else:
      output_shape = (batch_size, out_height, out_width, self.filters)
      strides = (1, stride_h, stride_w, 1)

    output_shape_tensor = array_ops.stack(output_shape)
    outputs = nn.conv2d_transpose(
        inputs,
        self.kernel,
        output_shape_tensor,
        strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, ndim=4))

    if context.in_graph_mode():
      # Infer the static output shape:
      out_shape = inputs.get_shape().as_list()
      out_shape[c_axis] = self.filters
      out_shape[h_axis] = utils.deconv_output_length(out_shape[h_axis],
                                                     kernel_h,
                                                     self.padding,
                                                     stride_h)
      out_shape[w_axis] = utils.deconv_output_length(out_shape[w_axis],
                                                     kernel_w,
                                                     self.padding,
                                                     stride_w)
      outputs.set_shape(out_shape)

    if self.use_bias:
      outputs = nn.bias_add(
          outputs,
          self.bias,
          data_format=utils.convert_data_format(self.data_format, ndim=4))

    if self.activation is not None:
      return self.activation(outputs)
    return outputs
Example #6
Source File: convolutional.py    From keras-lambda with MIT License 4 votes vote down vote up 
def call(self, inputs):
    inputs_shape = array_ops.shape(inputs)
    batch_size = inputs_shape[0]
    if self.data_format == 'channels_first':
      c_axis, h_axis, w_axis = 1, 2, 3
    else:
      c_axis, h_axis, w_axis = 3, 1, 2

    height, width = inputs_shape[h_axis], inputs_shape[w_axis]
    kernel_h, kernel_w = self.kernel_size
    stride_h, stride_w = self.strides

    def get_deconv_dim(dim_size, stride_size, kernel_size, padding):
      if isinstance(dim_size, ops.Tensor):
        dim_size = math_ops.multiply(dim_size, stride_size)
      elif dim_size is not None:
        dim_size *= stride_size

      if padding == 'valid' and dim_size is not None:
        dim_size += max(kernel_size - stride_size, 0)
      return dim_size

    # Infer the dynamic output shape:
    out_height = get_deconv_dim(height, stride_h, kernel_h, self.padding)
    out_width = get_deconv_dim(width, stride_w, kernel_w, self.padding)

    if self.data_format == 'channels_first':
      output_shape = (batch_size, self.filters, out_height, out_width)
      strides = (1, 1, stride_h, stride_w)
    else:
      output_shape = (batch_size, out_height, out_width, self.filters)
      strides = (1, stride_h, stride_w, 1)

    output_shape_tensor = array_ops.stack(output_shape)
    outputs = nn.conv2d_transpose(
        inputs,
        self.kernel,
        output_shape_tensor,
        strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, ndim=4))

    # Infer the static output shape:
    out_shape = inputs.get_shape().as_list()
    out_shape[c_axis] = self.filters
    out_shape[h_axis] = get_deconv_dim(
        out_shape[h_axis], stride_h, kernel_h, self.padding)
    out_shape[w_axis] = get_deconv_dim(
        out_shape[w_axis], stride_w, kernel_w, self.padding)
    outputs.set_shape(out_shape)

    if self.bias:
      outputs = nn.bias_add(
          outputs,
          self.bias,
          data_format=utils.convert_data_format(self.data_format, ndim=4))

    if self.activation is not None:
      return self.activation(outputs)
    return outputs