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Tf.nn.conv2d_transpose का उपयोग मध्यस्थ बैच आकार के लिए और स्वचालित आउटपुट आकृति गणना के साथ करना।

आउटपुट आकार की गणना करने और अज्ञात बैच आकार (जब input.get_shape () है ?, (, एच, डब्ल्यू, सी) या (?, सी, एच, डब्ल्यू) के साथ tf.nn.conv2d_transpose का उपयोग करने की कठिनाइयों को दूर करने का उदाहरण। )।

def upconvolution (input, output_channel_size, filter_size_h, filter_size_w,
                   stride_h, stride_w, init_w, init_b, layer_name, 
                   dtype=tf.float32, data_format="NHWC", padding='VALID'):
    with tf.variable_scope(layer_name):
      #calculation of the output_shape:
      if data_format == "NHWC":
        input_channel_size = input.get_shape().as_list()[3]
        input_size_h = input.get_shape().as_list()[1]
        input_size_w = input.get_shape().as_list()[2]
        stride_shape = [1, stride_h, stride_w, 1]
        if padding == 'VALID':
          output_size_h = (input_size_h - 1)*stride_h + filter_size_h
          output_size_w = (input_size_w - 1)*stride_w + filter_size_w
        elif padding == 'SAME':
          output_size_h = (input_size_h - 1)*stride_h + 1
          output_size_w = (input_size_w - 1)*stride_w + 1
        else:
          raise ValueError("unknown padding")
        output_shape = tf.stack([tf.shape(input)[0], 
                                output_size_h, output_size_w, 
                                output_channel_size])
      elif data_format == "NCHW":
        input_channel_size = input.get_shape().as_list()[1]
        input_size_h = input.get_shape().as_list()[2]
        input_size_w = input.get_shape().as_list()[3]
        stride_shape = [1, 1, stride_h, stride_w]
        if padding == 'VALID':
          output_size_h = (input_size_h - 1)*stride_h + filter_size_h
          output_size_w = (input_size_w - 1)*stride_w + filter_size_w
        elif padding == 'SAME':
          output_size_h = (input_size_h - 1)*stride_h + 1
          output_size_w = (input_size_w - 1)*stride_w + 1
        else:
          raise ValueError("unknown padding")
        output_shape = tf.stack([tf.shape(input)[0], 
                                output_channel_size, 
                                output_size_h, output_size_w])
      else:
        raise ValueError("unknown data_format")

      #creating weights:
      shape = [filter_size_h, filter_size_w, 
               output_channel_size, input_channel_size]
      W_upconv = tf.get_variable("w", shape=shape, dtype=dtype,
                                 initializer=init_w)
      
      shape=[output_channel_size]
      b_upconv = tf.get_variable("b", shape=shape, dtype=dtype, 
                                 initializer=init_b)
      
      upconv = tf.nn.conv2d_transpose(input, W_upconv, output_shape, stride_shape,
                                      padding=padding,
                                      data_format=data_format)
      output = tf.nn.bias_add(upconv, b_upconv, data_format=data_format)
      
      #Now output.get_shape() is equal (?,?,?,?) which can become a problem in the 
      #next layers. This can be repaired by reshaping the tensor to its shape:
      output = tf.reshape(output, output_shape)
      #now the shape is back to (?, H, W, C) or (?, C, H, W)
      
      return output


Modified text is an extract of the original Stack Overflow Documentation
के तहत लाइसेंस प्राप्त है CC BY-SA 3.0
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