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LeNet.py
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LeNet.py
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import numpy as np
import os
import numpy as np
from PIL import Image
import tensorflow as tf
from tensorflow.keras.utils import to_categorical
from keras import models, layers, regularizers
from keras.optimizers import RMSprop
from keras.datasets import mnist
import matplotlib.pyplot as plt
train_path = './data_800/train_600/'
train_txt = './data_800/train_600.txt'
x_train_savepath = './data_800/x_train.npy'
y_train_savepath = './data_800/y_train.npy'
test_path = './data_800/test_200/'
test_txt = './data_800/test_200.txt'
x_test_savepath = './data_800/x_test.npy'
y_test_savepath = './data_800/y_test.npy'
def generateds(path, txt):
f = open(txt, 'r') # 以只读形式打开txt文件
contents = f.readlines() # 读取文件中所有行
f.close() # 关闭txt文件
x, y_ = [], [] # 建立空列表
for content in contents: # 逐行取出
value = content.split() # 以空格分开，图片路径为value[0] , 标签为value[1] , 存入列表
img_path = path + value[0] # 拼出图片路径和文件名
img = Image.open(img_path) # 读入图片
img = np.array(img.convert('L')) # 图片变为8位宽灰度值的np.array格式
img = img / 255. # 数据归一化 （实现预处理）
x.append(img) # 归一化后的数据，贴到列表x
y_.append(value[1]) # 标签贴到列表y_
print('loading : ' + content) # 打印状态提示
x = np.array(x) # 变为np.array格式
y_ = np.array(y_) # 变为np.array格式
y_ = y_.astype(np.int64) # 变为64位整型
return x, y_ # 返回输入特征x，返回标签y_
if os.path.exists(x_train_savepath) and os.path.exists(y_train_savepath) and os.path.exists(
x_test_savepath) and os.path.exists(y_test_savepath):
print('-------------Load Datasets-----------------')
x_train_save = np.load(x_train_savepath)
y_train = np.load(y_train_savepath)
x_test_save = np.load(x_test_savepath)
y_test = np.load(y_test_savepath)
x_train = np.reshape(x_train_save, (len(x_train_save), 28, 28))
x_test = np.reshape(x_test_save, (len(x_test_save), 28, 28))
else:
print('-------------Generate Datasets-----------------')
x_train, y_train = generateds(train_path, train_txt)
x_test, y_test = generateds(test_path, test_txt)
print('-------------Save Datasets-----------------')
x_train_save = np.reshape(x_train, (len(x_train), -1))
x_test_save = np.reshape(x_test, (len(x_test), -1))
np.save(x_train_savepath, x_train_save)
np.save(y_train_savepath, y_train)
np.save(x_test_savepath, x_test_save)
np.save(y_test_savepath, y_test)
np.random.shuffle(x_train)
np.random.shuffle(x_test)
####with the fully connected networks#####
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='sigmoid'),
tf.keras.layers.Dense(84, activation='sigmoid'),
tf.keras.layers.Dense(42, activation='tanh'),
tf.keras.layers.Dense(4, activation='softmax')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['sparse_categorical_accuracy'])
checkpoint_save_path = "./checkpoint1/LeNet.ckpt"
if os.path.exists(checkpoint_save_path + '.index'):
print('-------------load the model-----------------')
model.load_weights(checkpoint_save_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
save_weights_only=True,
save_best_only=True)
history = model.fit(x_train, y_train, batch_size=32, epochs=1000,
validation_data=(x_test, y_test), validation_freq=1)
model.summary()
# print(model.trainable_variables)
file = open('./weights_LeNet.txt', 'w')
for v in model.trainable_variables:
file.write(str(v.name) + '\n')
file.write(str(v.shape) + '\n')
file.write(str(v.numpy()) + '\n')
file.close()
score = model.evaluate(x_train, y_train, verbose=1)
print('Train accuracy', score[1])
# 显示训练集和验证集的acc和loss曲线
acc = history.history['sparse_categorical_accuracy']
val_acc = history.history['val_sparse_categorical_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.savefig('lenet_3l.pdf')
plt.close()
plt.show()
'''
train_images = x_train.reshape((300, 28*28)).astype('float')
test_images = x_test.reshape((100, 28*28)).astype('float')
print(y_train[0])
print("*"*40)
train_labels = to_categorical(y_train, 2)
print(train_labels[0])
test_labels = to_categorical(y_test, 2)
print("标签的具体数值",test_labels[0])
network = models.Sequential()
network.add(layers.Dense(units=128, activation='relu', input_shape=(28*28, ),
kernel_regularizer=regularizers.l1(0.0001)))
#network.add(layers.Dropout(0.01))
network.add(layers.Dense(units=30, activation='relu',
kernel_regularizer=regularizers.l1(0.0001)))
#network.add(layers.Dropout(0.01))
network.add(layers.Dense(units=2, activation='softmax'))
network.summary()
#神经网络的训练
network.compile(optimizer=RMSprop(learning_rate=0.001),
loss='categorical_crossentropy',metrics=['accuracy'])
network.fit(train_images, train_labels, epochs=300, batch_size=128, verbose=2)
#print(network.summary())
#来测试一下模型的性能
y_pre = network.predict(test_images[:5])
print(y_pre, "\n", test_labels[:5])
test_loss, test_accuracy = network.evaluate(test_images, test_labels)
print("test_loss", test_loss, " test_accuracy", test_accuracy)
'''