# Extracting style and content - 2

We shall now use the class `StyleContentModel` defined in the previous slide.

When called on an image, this model returns the gram matrix (style) of the `style_layers` and content of the `content_layers`:

Note:

• `tf.constant()` creates a constant tensor from a tensor-like object.

• The `output` returned by the `result` items are of type `tensorflow.python.framework.ops.EagerTensor`. So we shall convert that into NumPy array and find the mathematical statistics - like minimum value, mean value, etc - of each output.

INSTRUCTIONS
• Instantiate the class `StyleContentModel` and pass the `style_layers` and `content_layers`.

``````extractor = << your code comes here >>(style_layers, content_layers)
``````
• Now pass the `tf.constant(content_image)` to the `extractor` and get the `results` - which holds the content and style representations of the `content_image`.

``````results = << your code comes here >>(tf.constant(content_image))
``````
• Let us see the statistics of the gram-matrices returned for the content image.

``````for name, output in sorted(results['style'].items()):
print("  ", name)
print("    shape: ", output.numpy().shape)
print("    min: ", output.numpy().min())
print("    max: ", output.numpy().max())
print("    mean: ", output.numpy().mean())
print()
``````
• Let us see the statistics of the content matrices returned for the content image.

``````for name, output in sorted(results['content'].items()):
print("  ", name)
print("    shape: ", output.numpy().shape)
print("    min: ", output.numpy().min())
print("    max: ", output.numpy().max())
print("    mean: ", output.numpy().mean())
``````

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