Convolution

import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras import models

mnist = tf.keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
training_images=training_images.reshape(60000, 28, 28, 1)

training_images=training_images / 255.0
test_images = test_images.reshape(10000, 28, 28, 1)

test_images=test_images/255.0
model = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),
  tf.keras.layers.MaxPooling2D(2, 2),
  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
  tf.keras.layers.MaxPooling2D(2,2),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.summary()
model.fit(training_images, training_labels, epochs=5)
test_loss = model.evaluate(test_images, test_labels)


f, axarr = plt.subplots(3,5)
layer_outputs = [layer.output for layer in model.layers] # 아마 레이어 각각 output 반환..

activation_model = tf.keras.models.Model(inputs = model.input, outputs =layer_outputs)
for x in range(0,4):  #4개의 레이어 통과할 때마다의 변화를 관찰
  f1 = activation_model.predict(test_images[0].reshape(1,28,28,1))[x] # 첫번째 테스트 이미지가 변환돼서 들어간다,
  # 리턴은 4번의 레이어 통과에 따른 output 이미지
  axarr[0,x].imshow(f1[0,:,:,1])
  f2 = activation_model.predict(test_images[3].reshape(1,28,28,1))[x]
  axarr[1,x].imshow(f2[0,:,:,1])
  f3 = activation_model.predict(test_images[6].reshape(1,28,28,1))[x]
  axarr[2,x].imshow(f3[0,:,:,1])

axarr[0,4].imshow(test_images[0])
axarr[1,4].imshow(test_images[3])
axarr[2,4].imshow(test_images[6])
plt.show()