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用keras搭建了一个简单的卷积神经网络，就一个卷积层，训练Cifar10,效果依然不错，卷积网络的强大可得知，改一改路径就能训练成功了
import tensorflow as tf
import os
import numpy as np
from matplotlib import pyplot as plt
from tensorflow.keras.layers import Conv2D,BatchNormalization,Activation,MaxPool2D,Dropout,Flatten,Dense
from tensorflow.keras import Model
np.set_printoptions(threshold = np.inf)
cifar10 = tf.keras.datasets.cifar10
(x_train,y_train),(x_test,y_test) = cifar10.load_data()
x_train,x_test = x_train/255.0,x_test/255.0
class Baseline(Model):
def __init__(self):
super(Baseline,self).__init__()
self.c1 = Conv2D(filters = 6,kernel_size = (5,5),padding = 'same')
self.b1 = BatchNormalization()
self.a1 = Activation('relu')
self.p1 = MaxPool2D(pool_size = (2,2),strides= 2,padding = 'same')
self.d1 = Dropout(0.2)
self.flatten = Flatten()
self.f1 = Dense(128,activation = 'relu')
self.d2 = Dropout(0.2)
self.f2 = Dense(10,activation = 'softmax')
def call(self,x):
x = self.c1(x)
x = self.b1(x)
x = self.a1(x)
x = self.p1(x)
x = self.d1(x)
x = self.flatten(x)
x = self.f1(x)
x = self.d2(x)
y = self.f2(x)
return y
model = Baseline()
model.compile(optimizer = 'adam',
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics = ['sparse_categorical_accuracy'])
checkpoint_save_path = "C:/Users/15792/Desktop/AI/Tensorflow0.0/cifar10_model/cifar_model"
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 = 5,validation_data = (x_test,y_test),validation_freq = 1,
callbacks = [cp_callback])
model.summary()
file = open("C:/Users/15792/Desktop/AI/Tensorflow0.0/cifar10_model.weights.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()
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 = 'Trainig 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.show()
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