mirror of
https://github.com/wassname/keras-contrib.git
synced 2026-06-27 16:10:11 +08:00
Andrew Hundt
85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
'''
|
|
Trains a DenseNet-40-12 model on the CIFAR-10 Dataset.
|
|
|
|
Gets a 94.84% accuracy score after 100 epochs.
|
|
'''
|
|
from __future__ import absolute_import
|
|
from __future__ import print_function
|
|
from __future__ import division
|
|
|
|
import numpy as np
|
|
|
|
from keras import backend as K
|
|
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
|
|
from keras.datasets import cifar10
|
|
from keras.optimizers import Adam
|
|
from keras.preprocessing.image import ImageDataGenerator
|
|
from keras.utils import np_utils
|
|
from keras_contrib.applications import DenseNet
|
|
|
|
batch_size = 64
|
|
nb_classes = 10
|
|
epochs = 100
|
|
|
|
img_rows, img_cols = 32, 32
|
|
img_channels = 3
|
|
|
|
# Parameters for the DenseNet model builder
|
|
img_dim = (img_channels, img_rows, img_cols) if K.image_data_format() == 'channels_first' else (img_rows, img_cols, img_channels)
|
|
depth = 40
|
|
nb_dense_block = 3
|
|
growth_rate = 12
|
|
nb_filter = 16
|
|
dropout_rate = 0.0 # 0.0 for data augmentation
|
|
|
|
# Create the model (without loading weights)
|
|
model = DenseNet(depth=depth, nb_dense_block=nb_dense_block,
|
|
growth_rate=growth_rate, nb_filter=nb_filter,
|
|
dropout_rate=dropout_rate,
|
|
input_shape=img_dim,
|
|
weights=None)
|
|
print('Model created')
|
|
|
|
model.summary()
|
|
|
|
optimizer = Adam(lr=1e-3) # Using Adam instead of SGD to speed up training
|
|
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['acc'])
|
|
print('Finished compiling')
|
|
|
|
(trainX, trainY), (testX, testY) = cifar10.load_data()
|
|
|
|
trainX = trainX.astype('float32')
|
|
testX = testX.astype('float32')
|
|
|
|
trainX /= 255.
|
|
testX /= 255.
|
|
|
|
Y_train = np_utils.to_categorical(trainY, nb_classes)
|
|
Y_test = np_utils.to_categorical(testY, nb_classes)
|
|
|
|
generator = ImageDataGenerator(rotation_range=15,
|
|
width_shift_range=5. / 32,
|
|
height_shift_range=5. / 32)
|
|
|
|
generator.fit(trainX, seed=0)
|
|
|
|
weights_file = 'DenseNet-40-12-CIFAR-10.h5'
|
|
|
|
lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1),
|
|
cooldown=0, patience=10, min_lr=0.5e-6)
|
|
early_stopper = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=20)
|
|
model_checkpoint = ModelCheckpoint(weights_file, monitor='val_acc', save_best_only=True,
|
|
save_weights_only=True, mode='auto')
|
|
|
|
callbacks = [lr_reducer, early_stopper, model_checkpoint]
|
|
|
|
model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), steps_per_epoch=len(trainX) // batch_size,
|
|
epochs=epochs,
|
|
callbacks=callbacks,
|
|
validation_data=(testX, Y_test),
|
|
verbose=2)
|
|
|
|
scores = model.evaluate(testX, Y_test, batch_size=batch_size)
|
|
print('Test loss : ', scores[0])
|
|
print('Test accuracy : ', scores[1])
|