Implementing convolutions in Keras

Image Modeling with Keras

Ariel Rokem

Senior Data Scientist, University of Washington

Keras Convolution layer

from keras.layers import Conv2D
Conv2D(10, kernel_size=3, activation='relu')

Integrating convolution layers into a network

from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten

model = Sequential()
model.add(Conv2D(10, kernel_size=3, activation='relu', 
          input_shape=(img_rows, img_cols, 1)))

model.add(Flatten())
model.add(Dense(3, activation='softmax'))

Our CNN

Fitting a CNN

model.compile(optimizer='adam', 
              loss='categorical_crossentropy', 
              metrics=['accuracy'])

train_data.shape

(50, 28, 28, 1)

model.fit(train_data, train_labels, validation_split=0.2, 
          epochs=3)

model.evaluate(test_data, test_labels, epochs=3)

Let's practice!

Image Modeling with Keras