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Legal info Not released under the GPL References External links Technical info Category:Pascal software Category:Utilities for macOS Category:Utilities for Windows Category:Utilities for LinuxQ: tensorflow's tf.contrib.layers.conv2d_transpose() accepts only ndarray for input, not tensor. I want to do an operation like this tf.contrib.layers.conv2d_transpose(original_tf_image, output_channels) where original_tf_image is a tensor of shape (B, C, D) and output_channels is an int. what I want is to do an operation like the one above for every pixel in the tensor. I guess I should use a loop. Unfortunately, I can't see how to do this. A: Your best bet is probably to do it with tf.map_fn. import tensorflow as tf def extract_conv_transpose(image): height, width, channels = tf.shape(image) # Note: Doesn't actually need to be a tf.Tensor; it can be a numpy array. # But that's what you have, so that's what we're going to use. original_tf_image = image.astype('float32') # Note: This one is a bit trickier to get into a Numpy array. # You may need to use a c-style cast first. numpy_image = tf.cast(original_tf_image, tf.float32) # The returned value is a tensor with shape (batch, width, height, channels) # that contains the input image transformed by the convolution. # The returned value is a tensor with shape (batch, channels, width, height) # that contains the output of the convolution. return tf.contrib.layers.conv2d_transpose(numpy_image, 1) if __name__ == '__main

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