def train(data,
file_name,
nlayer,
num_epochs=10,
batch_size=128,
train_temp=1,
init=None,
activation=tf.nn.relu):
"""
Train a n-layer CNN for MNIST and CIFAR
"""
inputs = Input(shape=(28, 28, 1))
if nlayer == 2:
x = Residual2(8, activation)(inputs)
x = Lambda(activation)(x)
x = Residual2(16, activation)(x)
x = Lambda(activation)(x)
x = AveragePooling2D(pool_size=7)(x)
x = Flatten()(x)
x = Dense(10)(x)
if nlayer == 3:
x = Residual2(8, activation)(inputs)
x = Lambda(activation)(x)
x = Residual(8, activation)(x)
x = Lambda(activation)(x)
x = Residual2(16, activation)(x)
x = Lambda(activation)(x)
x = AveragePooling2D(pool_size=7)(x)
x = Flatten()(x)
x = Dense(10)(x)
if nlayer == 4:
x = Residual2(8, activation)(inputs)
x = Lambda(activation)(x)
x = Residual(8, activation)(x)
x = Lambda(activation)(x)
x = Residual2(16, activation)(x)
x = Lambda(activation)(x)
x = Residual(16, activation)(x)
x = Lambda(activation)(x)
x = AveragePooling2D(pool_size=7)(x)
x = Flatten()(x)
x = Dense(10)(x)
if nlayer == 5:
x = Residual2(8, activation)(inputs)
x = Lambda(activation)(x)
x = Residual(8, activation)(x)
x = Lambda(activation)(x)
x = Residual(8, activation)(x)
x = Lambda(activation)(x)
x = Residual2(16, activation)(x)
x = Lambda(activation)(x)
x = Residual(16, activation)(x)
x = Lambda(activation)(x)
x = AveragePooling2D(pool_size=7)(x)
x = Flatten()(x)
x = Dense(10)(x)
model = Model(inputs=inputs, outputs=x)
# load initial weights when given
if init != None:
model.load_weights(init)
# define the loss function which is the cross entropy between prediction and true label
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
logits=predicted /
train_temp)
# initiate the Adam optimizer
sgd = Adam()
# compile the Keras model, given the specified loss and optimizer
model.compile(loss=fn, optimizer=sgd, metrics=['accuracy'])
model.summary()
# run training with given dataset, and print progress
history = model.fit(data.train_data,
data.train_labels,
batch_size=batch_size,
validation_data=(data.validation_data,
data.validation_labels),
epochs=num_epochs,
shuffle=True)
# save model to a file
if file_name != None:
model.save(file_name)
return {'model': model, 'history': history}
output = lenet(myinput)
# 建立模型
model = Model(myinput, output)
# 定义优化器，这里选用Adam优化器，学习率设置为0.0003
adam = Adam(lr=0.0003)
# 编译模型
model.compile(optimizer=adam,
loss="categorical_crossentropy",
metrics=['accuracy'])
# 准备数据
# 获取输入的图像
X = GetTrainDataByLabel('data')
# 获取图像的label，这里使用to_categorical函数返回one-hot之后的label
Y = to_categorical(GetTrainDataByLabel('labels'))
# 开始训练模型，batch设置为200，一共50个epoch
model.fit(X,
Y,
200,
50,
1,
callbacks=[
TensorBoard('./LeNet/log', write_images=1, histogram_freq=1)
],
validation_split=0.2,
shuffle=True)
# 保存模型
model.save("lenet-no-activation-model.h5")
