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cifar10.py
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cifar10.py
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from __future__ import print_function
import os.path
import densenet
import numpy as np
import sklearn.metrics as metrics
from keras.datasets import cifar10
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras import backend as K
batch_size = 64
nb_classes = 10
nb_epoch = 300
img_rows, img_cols = 32, 32
img_channels = 3
img_dim = (img_channels, img_rows, img_cols) if K.image_dim_ordering() == "th" 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
model = densenet.create_dense_net(nb_classes, img_dim, depth, nb_dense_block, growth_rate, nb_filter,
dropout_rate=dropout_rate)
print("Model created")
model.summary()
optimizer = Adam(lr=1e-4) # Using Adam instead of SGD to speed up training
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=["accuracy"])
print("Finished compiling")
print("Building model...")
(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)
# Load model
weights_file="weights/DenseNet-40-12CIFAR10-tf.h5"
if os.path.exists(weights_file):
model.load_weights(weights_file)
print("Model loaded.")
out_dir="weights/"
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=0.0001, 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), samples_per_epoch=len(trainX), nb_epoch=nb_epoch,
callbacks=callbacks,
validation_data=(testX, Y_test),
nb_val_samples=testX.shape[0], verbose=2)
yPreds = model.predict(testX)
yPred = np.argmax(yPreds, axis=1)
yTrue = testY
accuracy = metrics.accuracy_score(yTrue, yPred) * 100
error = 100 - accuracy
print("Accuracy : ", accuracy)
print("Error : ", error)