How do I explain this TensorFlow tf.nn.conv2d() layer shape?

Question

My Tensorflow convolutional layer has a shape I did not expect it to have and I do not see the mistake.

I am new to TensorFlow and want to use this function to create a convolutional layer:

def new_conv_layer(input,              # The previous layer.
                   num_input_channels, # Num. channels in prev. layer.
                   filter_size,        # Width and height of each filter.
                   num_filters,        # Number of filters.
                   use_pooling=True):  # Use 2x2 max-pooling.

    shape = [filter_size, filter_size, num_input_channels, num_filters]

    weights = new_weights(shape=shape)

    biases = new_biases(length=num_filters)

    layer = tf.nn.conv2d(input=input_,
                         filters=weights,
                         strides=[1, 1, 1, 1],
                         padding='SAME')

    layer += biases

    if use_pooling:

        layer = tf.nn.max_pool(input=layer,
                               ksize=[1, 2, 2, 1],
                               strides=[1, 2, 2, 1],
                               padding='SAME')


    layer = tf.nn.relu(layer)


    return layer, weights

But when I use it with

num_channels = 1
img_size = 28

x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])

# Convolutional Layer 1.
filter_size1 = 5          # Convolution filters are 5 x 5 pixels.
num_filters1 = 16         # There are 16 of these filters.

layer_conv1, weights_conv1 = new_conv_layer(input=x_image,
                                           num_input_channels=num_channels,
                                           filter_size=filter_size1,
                                           num_filters=num_filters1,
                                           use_pooling=True)

layer_conv1

I get this output:

<tf.Tensor 'Relu:0' shape=(None, 392, 392, 16) dtype=float32>

Because my images are of a square 28x28 shape and I apply 2x2 pooling, I would have expected this shape to be (None, 14, 14, 16).

Why is that not the case and how do I fix it?

Answer 1

in my case this line x = tf.compat.v1.placeholder(tf.float32, shape=[None, img_size_flat], name='x') was incorrect!

In particular img_size_flat was not the length of each "stretched" image, as it should have been.

img_size_flat = df.drop('label', axis=1).shape[1]