def gen_data_batch(source):
data_gen = gen_data(source)
while True:
image_batch = []
label_batch = []
for _ in range(batch_size):
image, label = next(data_gen)
image_batch.append(image)
label_batch.append(label)
yield np.array(image_batch), np.array(label_batch)
images = tf.placeholder(tf.float32, [None, 28, 28, 1])
labels = tf.placeholder(tf.float32, [None, 10])
net = MyNet({'data': images})
ip2 = net.layers['ip2']
pred = net.layers['prob']
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=ip2, labels=labels), 0)
opt = tf.train.RMSPropOptimizer(0.001)
train_op = opt.minimize(loss)
with tf.Session() as sess:
# Load the data
sess.run(tf.global_variables_initializer())
net.load('mynet.npy', sess)
data_gen = gen_data_batch(mnist.train)
# split the train and test images
train_file_path = imagePaths[:train_num]
print('train num is', len(train_file_path))
test_file_path = imagePaths[train_num:]
print('test num is', len(test_file_path))
# data ,label of train ,test
trainX, trainY = load_data(train_file_path)
testX, testY = load_data(test_file_path)
# data augmentation
aug = ImageDataGenerator(rotation_range=15, width_shift_range=0.1,
height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
horizontal_flip=True, fill_mode="nearest")
# initialize the model
print("compiling model...")
model = LeNet(width=norm_size, height=norm_size, depth=3, classes=CLASS_NUM)
# model = AlexNet(width=norm_size, height=norm_size, depth=3, classes=CLASS_NUM)
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
# model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
model.summary()
plot_model(model, to_file='model.png')
# train the network
print("training network...")
lr_decay = LearningRateScheduler(scheduler)
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=Batch_Size),
validation_data=(testX, testY), steps_per_epoch=len(trainX) // Batch_Size,
epochs=EPOCHS, verbose=1, callbacks=[lr_decay])
# save the model
print("save model...")
def gen_data_batch(source):
data_gen = gen_data(source)
while True:
image_batch = []
label_batch = []
for _ in range(batch_size):
image, label = next(data_gen)
image_batch.append(image)
label_batch.append(label)
yield np.array(image_batch), np.array(label_batch)
images = tf.placeholder(tf.float32, [batch_size, 28, 28, 1])
labels = tf.placeholder(tf.float32, [batch_size, 10])
net = MyNet({'data': images})
ip2 = net.layers['ip2']
pred = tf.nn.softmax(ip2)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(ip2, labels), 0)
opt = tf.train.RMSPropOptimizer(0.001)
train_op = opt.minimize(loss)
with tf.Session() as sess:
# Load the data
sess.run(tf.initialize_all_variables())
net.load('mynet.npy', sess)
data_gen = gen_data_batch(mnist.train)
for i in range(1000):