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- def put_kernels_on_grid (kernel, (grid_Y, grid_X), pad=1):
- '''Visualize conv. features as an image (mostly for the 1st layer).
- Place kernel into a grid, with some paddings between adjacent filters.
- Args:
- kernel: tensor of shape [Y, X, NumChannels, NumKernels]
- (grid_Y, grid_X): shape of the grid. Require: NumKernels == grid_Y * grid_X
- User is responsible of how to break into two multiples.
- pad: number of black pixels around each filter (between them)
- Return:
- Tensor of shape [(Y+pad)*grid_Y, (X+pad)*grid_X, NumChannels, 1].
- '''
- # pad X and Y
- x1 = tf.pad(kernel, tf.constant( [[pad,0],[pad,0],[0,0],[0,0]] ))
- # X and Y dimensions, w.r.t. padding
- Y = kernel.get_shape()[0] + pad
- X = kernel.get_shape()[1] + pad
- # put NumKernels to the 1st dimension
- x2 = tf.transpose(x1, (3, 0, 1, 2))
- # organize grid on Y axis
- x3 = tf.reshape(x2, tf.pack([grid_X, Y * grid_Y, X, 3]))
- # switch X and Y axes
- x4 = tf.transpose(x3, (0, 2, 1, 3))
- # organize grid on X axis
- x5 = tf.reshape(x4, tf.pack([1, X * grid_X, Y * grid_Y, 3]))
- # back to normal order (not combining with the next step for clarity)
- x6 = tf.transpose(x5, (2, 1, 3, 0))
- # to tf.image_summary order [batch_size, height, width, channels],
- # where in this case batch_size == 1
- x7 = tf.transpose(x6, (3, 0, 1, 2))
- # scale to [0, 1]
- x_min = tf.reduce_min(x7)
- x_max = tf.reduce_max(x7)
- x8 = (x7 - x_min) / (x_max - x_min)
- return x8
- #
- # ... and somewhere inside "def train():" after calling "inference()"
- #
- # Visualize conv1 features
- with tf.variable_scope('conv1') as scope_conv:
- tf.get_variable_scope().reuse_variables()
- weights = tf.get_variable('weights')
- grid_x = grid_y = 8 # to get a square grid for 64 conv1 features
- grid = put_kernels_on_grid (weights, (grid_y, grid_x))
- tf.image_summary('conv1/features', grid, max_images=1)
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