127 lines
3.6 KiB
Python
127 lines
3.6 KiB
Python
'''Transfer learning toy example:
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1- Train a simple convnet on the MNIST dataset the first 5 digits [0..4].
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2- Freeze convolutional layers and fine-tune dense layers
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for the classification of digits [5..9].
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Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_transfer_cnn.py
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Get to 99.8% test accuracy after 5 epochs
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for the first five digits classifier
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and 99.2% for the last five digits after transfer + fine-tuning.
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'''
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from __future__ import print_function
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import datetime
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import keras
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Activation, Flatten
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from keras.layers import Conv2D, MaxPooling2D
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from keras import backend as K
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now = datetime.datetime.now
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batch_size = 128
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num_classes = 5
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epochs = 5
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# input image dimensions
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img_rows, img_cols = 28, 28
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# number of convolutional filters to use
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filters = 32
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# size of pooling area for max pooling
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pool_size = 2
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# convolution kernel size
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kernel_size = 3
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if K.image_data_format() == 'channels_first':
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input_shape = (1, img_rows, img_cols)
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else:
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input_shape = (img_rows, img_cols, 1)
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def train_model(model, train, test, num_classes):
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x_train = train[0].reshape((train[0].shape[0],) + input_shape)
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x_test = test[0].reshape((test[0].shape[0],) + input_shape)
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x_train = x_train.astype('float32')
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x_test = x_test.astype('float32')
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x_train /= 255
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x_test /= 255
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print('x_train shape:', x_train.shape)
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print(x_train.shape[0], 'train samples')
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print(x_test.shape[0], 'test samples')
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# convert class vectors to binary class matrices
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y_train = keras.utils.to_categorical(train[1], num_classes)
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y_test = keras.utils.to_categorical(test[1], num_classes)
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model.compile(loss='categorical_crossentropy',
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optimizer='adadelta',
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metrics=['accuracy'])
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t = now()
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=epochs,
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verbose=1,
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validation_data=(x_test, y_test))
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print('Training time: %s' % (now() - t))
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score = model.evaluate(x_test, y_test, verbose=0)
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print('Test score:', score[0])
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print('Test accuracy:', score[1])
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# the data, shuffled and split between train and test sets
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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# create two datasets one with digits below 5 and one with 5 and above
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x_train_lt5 = x_train[y_train < 5]
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y_train_lt5 = y_train[y_train < 5]
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x_test_lt5 = x_test[y_test < 5]
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y_test_lt5 = y_test[y_test < 5]
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x_train_gte5 = x_train[y_train >= 5]
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y_train_gte5 = y_train[y_train >= 5] - 5
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x_test_gte5 = x_test[y_test >= 5]
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y_test_gte5 = y_test[y_test >= 5] - 5
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# define two groups of layers: feature (convolutions) and classification (dense)
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feature_layers = [
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Conv2D(filters, kernel_size,
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padding='valid',
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input_shape=input_shape),
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Activation('relu'),
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Conv2D(filters, kernel_size),
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Activation('relu'),
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MaxPooling2D(pool_size=pool_size),
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Dropout(0.25),
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Flatten(),
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]
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classification_layers = [
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Dense(128),
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Activation('relu'),
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Dropout(0.5),
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Dense(num_classes),
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Activation('softmax')
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]
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# create complete model
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model = Sequential(feature_layers + classification_layers)
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# train model for 5-digit classification [0..4]
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train_model(model,
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(x_train_lt5, y_train_lt5),
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(x_test_lt5, y_test_lt5), num_classes)
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# freeze feature layers and rebuild model
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for l in feature_layers:
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l.trainable = False
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# transfer: train dense layers for new classification task [5..9]
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train_model(model,
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(x_train_gte5, y_train_gte5),
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(x_test_gte5, y_test_gte5), num_classes)
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