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import keras | |
from keras import layers | |
from keras import models | |
model = models.Sequential() | |
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1))) | |
model.add(layers.MaxPooling2D((2, 2))) | |
model.add(layers.Conv2D(64, (3, 3), activation='relu')) | |
model.add(layers.MaxPooling2D((2, 2))) | |
model.add(layers.Conv2D(64, (3, 3), activation='relu')) | |
#model.summary() | |
# add fully-connected layers at the end of the model | |
model.add(layers.Flatten()) | |
model.add(layers.Dense(64, activation='relu')) | |
model.add(layers.Dense(10, activation='softmax')) | |
model.summary() | |
from keras.datasets import mnist | |
#from keras.utils import to_categorical | |
from tensorflow.keras.utils import to_categorical | |
(train_images, train_labels), (test_images, test_labels) = mnist.load_data() | |
train_images = train_images.reshape((60000, 28, 28, 1)) | |
train_images = train_images.astype('float32') / 255 | |
test_images = test_images.reshape((10000, 28, 28, 1)) | |
test_images = test_images.astype('float32') / 255 | |
train_labels = to_categorical(train_labels) | |
test_labels = to_categorical(test_labels) | |
# compile and train the model | |
model.compile(optimizer='rmsprop', | |
loss='categorical_crossentropy', | |
metrics=['accuracy']) | |
model.fit(train_images, train_labels, epochs=5, batch_size=64) | |
test_loss, test_acc = model.evaluate(test_images, test_labels) |