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Chapter 6
The images need to be normalized before we train the network. For better
performance you can even add jitter to the images:
def normalize(input_image, label):
input_image = tf.cast(input_image, tf.float32)
input_image = (input_image / 127.5) - 1
return input_image
The preceding function when applied to images will normalize them in the range
[-1,1]. Let us apply this to our train and test datasets and create a data generator that
will provide images for training in batches:
train_A = train_A.map(normalize, num_parallel_calls=AUTOTUNE).cache().
shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
train_B = train_B.map(normalize, num_parallel_calls=AUTOTUNE).cache().
shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
test_A = test_A.map(normalize, num_parallel_calls=AUTOTUNE).cache().
shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
test_B = test_B.map(normalize, num_parallel_calls=AUTOTUNE).cache().
shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
In the preceding code the argument num_parallel_calls allows one to take benefit
from multiple CPU cores in the system, one should set its value to the number
of CPU cores in your system. If you are not sure, use the AUTOTUNE = tf.data.
experimental.AUTOTUNE value so that TensorFlow dynamically determines the
right number for you.
Before moving ahead with the model definition, let us see the images. Each image is
processed before plotting so that its intensity is normal:
inpA = next(iter(train_A))
inpB = next(iter(train_B))
plt.subplot(121)
plt.title("Train Set A")
plt.imshow(inpA[0]*0.5 + 0.5)
plt.subplot(122)
plt.title("Train Set B")
plt.imshow(inpB[0]*0.5 + 0.5)
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