example script keras lenet model mnist

skluc/examples/keras_fc_cnn_mnist.py

+import numpy as np
+from keras import optimizers
+from keras.models import Sequential
+from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D, Dropout
+from keras.callbacks import LearningRateScheduler, TensorBoard
+from keras.preprocessing.image import ImageDataGenerator
+from keras.regularizers import l2
+import skluc.data.mldatasets as dataset
+
+batch_size = 128
+epochs = 200
+iterations = 391
+weight_decay = 0.0001
+
+log_filepath = './lenet_dp_da_wd'
+
+
+def build_model():
+    model = Sequential()
+    model.add(Conv2D(32, (5, 5), padding='valid', activation='relu', kernel_initializer='he_normal',
+                     kernel_regularizer=l2(weight_decay), input_shape=(28, 28, 1), name="conv_1"))
+    model.add(MaxPooling2D((2, 2), strides=(2, 2), name="conv_pool_1"))
+    model.add(Conv2D(64, (5, 5), padding='valid', activation='relu', kernel_initializer='he_normal',
+                     kernel_regularizer=l2(weight_decay), name="conv_2"))
+    model.add(MaxPooling2D((2, 2), strides=(2, 2), name="conv_pool_2"))
+    model.add(Flatten(name='conv_flatten'))
+    model.add(Dense(1024, activation='relu', kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay)))
+    model.add(Dropout(0.4))
+    model.add(Dense(10, activation='softmax', kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay)))
+    sgd = optimizers.SGD(lr=.001, momentum=0.9, nesterov=True)
+    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
+    return model
+
+
+def scheduler(epoch):
+    if epoch <= 60:
+        return 0.05
+    if epoch <= 120:
+        return 0.01
+    if epoch <= 160:
+        return 0.002
+    return 0.0004
+
+
+if __name__ == '__main__':
+
+    # load data
+    validation_size = 10000
+    seed = 0
+    data = dataset.MnistDataset(validation_size=validation_size, seed=seed)
+    data.load()
+    data.normalize()
+    data.to_one_hot()
+    data.to_image()
+    data.data_astype(np.float32)
+    data.labels_astype(np.float32)
+
+    (x_train, y_train), (x_test, y_test) = data.train, data.test
+
+    # build network
+    model = build_model()
+    print(model.summary())
+
+    # set callback
+    tb_cb = TensorBoard(log_dir=log_filepath, histogram_freq=0)
+    change_lr = LearningRateScheduler(scheduler)
+    cbks = [change_lr, tb_cb]
+
+    # using real-time data augmentation
+    print('Using real-time data augmentation.')
+    datagen = ImageDataGenerator(horizontal_flip=True,
+                                 width_shift_range=0.125, height_shift_range=0.125, fill_mode='constant', cval=0.)
+
+    datagen.fit(x_train)
+
+    # start traing
+    model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
+                        steps_per_epoch=iterations,
+                        epochs=epochs,
+                        callbacks=cbks,
+                        validation_data=(x_test, y_test))
+    # save model
+    model.save('lenet.h5')
+    print("Final evaluation on test set: {}".format(model.evaluate(x_test, y_test)))