mirror of
https://github.com/wassname/keras-contrib.git
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83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
'''
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Trains a DenseNet-40-12 model on the CIFAR-10 Dataset.
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Gets a 94.84% accuracy score after 100 epochs.
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'''
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from __future__ import absolute_import
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from __future__ import print_function
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from __future__ import division
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import numpy as np
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import sklearn.metrics as metrics
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from keras import backend as K
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from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
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from keras.datasets import cifar10
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from keras.optimizers import Adam
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from keras.preprocessing.image import ImageDataGenerator
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from keras.utils import np_utils
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from keras_contrib.applications import DenseNet
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batch_size = 64
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nb_classes = 10
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nb_epoch = 100
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img_rows, img_cols = 32, 32
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img_channels = 3
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# Parameters for the DenseNet model builder
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img_dim = (img_channels, img_rows, img_cols) if K.image_dim_ordering() == "th" else (img_rows, img_cols, img_channels)
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depth = 40
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nb_dense_block = 3
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growth_rate = 12
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nb_filter = 16
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dropout_rate = 0.0 # 0.0 for data augmentation
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# Create the model (without loading weights)
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model = DenseNet(depth, nb_dense_block, growth_rate, nb_filter, dropout_rate=dropout_rate,
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input_shape=img_dim, weights=None)
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print("Model created")
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model.summary()
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optimizer = Adam(lr=1e-3) # Using Adam instead of SGD to speed up training
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model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=["accuracy"])
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print("Finished compiling")
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(trainX, trainY), (testX, testY) = cifar10.load_data()
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trainX = trainX.astype('float32')
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testX = testX.astype('float32')
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trainX /= 255.
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testX /= 255.
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Y_train = np_utils.to_categorical(trainY, nb_classes)
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Y_test = np_utils.to_categorical(testY, nb_classes)
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generator = ImageDataGenerator(rotation_range=15,
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width_shift_range=5. / 32,
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height_shift_range=5. / 32)
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generator.fit(trainX, seed=0)
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weights_file = "DenseNet-40-12-CIFAR-10.h5"
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lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1),
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cooldown=0, patience=10, min_lr=0.5e-6)
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early_stopper = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=20)
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model_checkpoint = ModelCheckpoint(weights_file, monitor="val_acc", save_best_only=True,
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save_weights_only=True, mode='auto')
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callbacks = [lr_reducer, early_stopper, model_checkpoint]
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model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), samples_per_epoch=len(trainX),
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nb_epoch=nb_epoch,
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callbacks=callbacks,
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validation_data=(testX, Y_test),
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nb_val_samples=testX.shape[0], verbose=2)
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scores = model.evaluate(testX, Y_test, batch_size=batch_size)
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print("Test loss : ", scores[0])
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print("Test accuracy : ", scores[1])
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