Created
February 26, 2021 02:51
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# GRADED FUNCTION: compute_layer_style_cost | |
def compute_layer_style_cost(a_S, a_G): | |
""" | |
Arguments: | |
a_S -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image S | |
a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image G | |
Returns: | |
J_style_layer -- tensor representing a scalar value, style cost defined above by equation (2) | |
""" | |
### START CODE HERE ### | |
# Retrieve dimensions from a_G (≈1 line) | |
m, n_H, n_W, n_C = a_G.get_shape().as_list() | |
# Reshape the images to have them of shape (n_C, n_H*n_W) (≈2 lines) | |
a_S = tf.transpose(tf.reshape(a_S, shape=[n_H*n_W, n_C])) | |
a_G = tf.transpose(tf.reshape(a_G, shape=[n_H*n_W, n_C])) | |
# Computing gram_matrices for both images S and G (≈2 lines) | |
GS = gram_matrix(a_S) | |
GG = gram_matrix(a_G) | |
# Computing the loss (≈1 line) | |
J_style_layer = 1/(4* n_C**2 *(n_H*n_W)**2)*tf.reduce_sum(tf.square(tf.subtract(GS, GG))) | |
### END CODE HERE ### | |
return J_style_layer |
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