DLS C4 W4 A2 - loss in training goes to -128130720.0

Hello guys,

During the training my loss goes to minus values. This is my training step function but it shouldnt be the cause:

def train_step(generated_image):
with tf.GradientTape() as tape:
# In this function you must use the precomputed encoded images a_S and a_C
# Compute a_G as the vgg_model_outputs for the current generated image

    #(1 line)
    a_G = vgg_model_outputs(generated_image)
    # Compute the style cost
    #(1 line)
    J_style = compute_style_cost(a_S, a_G)

    #(2 lines)
    # Compute the content cost
    J_content = compute_content_cost(a_C, a_G)
    # Compute the total cost
    J = total_cost(J_content, J_style)
grad = tape.gradient(J, generated_image)

optimizer.apply_gradients([(grad, generated_image)])
# For grading purposes
return J

Test results:

tf.Tensor(-128130720.0, shape=(), dtype=float32)
AssertionError: Unexpected cost for epoch 0: -128130720.0 != 10221.168

I think it should be connected with the style loss as I already had some problems with it before and when I change style loss value to some fixed number minus loss values disappear.

What is interesting is that during the style loss test I get the expected result, that is 14.017805.

This is my code for the style layer cost:

def compute_layer_style_cost(a_S, a_G):
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

J_style_layer -- tensor representing a scalar value, style cost defined above by equation (2)

# Retrieve dimensions from a_G (≈1 line)
m, n_H, n_W, n_C = a_G.get_shape().as_list()

a_S = tf.reshape(tf.transpose(a_S, perm=[3,1,2,0]), shape=[n_C, n_H * n_W])
a_G = tf.reshape(tf.transpose(a_G, perm=[3,1,2,0]), shape=[n_C, n_H * n_W])

# 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 = 1/(4 * tf.square(n_C) * tf.square(n_H * n_W)) 
J_style_layer_2 = tf.reduce_sum(tf.reduce_sum(tf.square(tf.subtract(GS, GG))))

J_style_layer_2 = tf.cast(J_style_layer_2, tf.float32)
J_style_layer_1 = tf.cast(J_style_layer_1, tf.float32)

J_style_layer = tf.math.multiply(J_style_layer_1, J_style_layer_2)


return J_style_layer

Without the cast rows:

J_style_layer_2 = tf.cast(J_style_layer_2, tf.float32)
J_style_layer_1 = tf.cast(J_style_layer_1, tf.float32)

it gives me error on test saying:

InvalidArgumentError: cannot compute Mul as input #1(zero-based) was expected to be a double tensor but is a float tensor [Op:Mul]

It would be nice if someone could help me in this.

Best regards,


  • Cost cannot be negative.
  • You haven’t set the alpha and beta values. Maybe you’re relying on the default argument values.
  • In compute_layer_style_cost(), why are you passing constant values for the “perm” argument, and why are you using the perm argument at all?
  • I think the order of your transpose and reshape may be backwards.
  • If you’re computing the squares of constant values, maybe an np function would be better to use. The tf functions are for tensors.
  • There may be some other errors in your style cost calculations.