Keras入门——（1）全连接神经网络FCN
Anaconda安装Keras：
conda install keras
安装完成：
在Jupyter Notebook中新建并执行代码：
import keras from keras.datasets import mnist # 从keras中导入mnist数据集 from keras.models import Sequential # 导入序贯模型 from keras.layers import Dense # 导入全连接层 from keras.optimizers import SGD # 导入优化函数 (x_train, y_train), (x_test, y_test) = mnist.load_data() # 加载mnist数据集
因为众所周知的原因，下载墙外的文件超时报错，参考 https://www.cnblogs.com/shinny/p/9283372.html 进行修改；
重复执行，报错：“TabError: inconsistent use of tabs and spaces in indentation”
参照 https://blog.csdn.net/qq_41096996/article/details/85947560 进行修改：
执行成功！
继续执行如下代码：
print(x_train.shape,y_train.shape) #(60000, 28, 28) (60000,) print(x_test.shape,y_test.shape) #(10000, 28, 28) (10000,)
继续执行：
import matplotlib.pyplot as plt # 导入可视化的包 im = plt.imshow(x_train[0],cmap='gray')
继续执行：
plt.show() y_train[0]
继续执行：
x_train = x_train.reshape(60000,784) # 将图片摊平，变成向量 x_test = x_test.reshape(10000,784) # 对测试集进行同样的处理 print(x_train.shape) #(60000, 784) print(x_test.shape) #(10000, 784)
继续执行：
x_train[0]
#array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18, 18, 18,
126, 136, 175, 26, 166, 255, 247, 127, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170, 253,
253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253,
253, 253, 253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 219, 253,
253, 253, 253, 253, 198, 182, 247, 241, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
80, 156, 107, 253, 253, 205, 11, 0, 43, 154, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 14, 1, 154, 253, 90, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 139, 253, 190, 2, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190, 253, 70,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35,
241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 45, 186, 253, 253, 150, 27, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 16, 93, 252, 253, 187,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 249,
253, 249, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 130,
183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 148,
229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114,
221, 253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 66,
213, 253, 253, 253, 253, 198, 81, 2, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 171,
219, 253, 253, 253, 253, 195, 80, 9, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 172,
226, 253, 253, 253, 253, 244, 133, 11, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
136, 253, 253, 253, 212, 135, 132, 16, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0], dtype=uint8)
继续执行：
x_train = x_train / 255
x_test = x_test / 255
x_train[0]
#array([0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.01176471, 0.07058824, 0.07058824,
0.07058824, 0.49411765, 0.53333333, 0.68627451, 0.10196078,
0.65098039, 1. , 0.96862745, 0.49803922, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.11764706, 0.14117647, 0.36862745, 0.60392157,
0.66666667, 0.99215686, 0.99215686, 0.99215686, 0.99215686,
0.99215686, 0.88235294, 0.6745098 , 0.99215686, 0.94901961,
0.76470588, 0.25098039, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.19215686, 0.93333333,
0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686,
0.99215686, 0.99215686, 0.99215686, 0.98431373, 0.36470588,
0.32156863, 0.32156863, 0.21960784, 0.15294118, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.07058824, 0.85882353, 0.99215686, 0.99215686,
0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.71372549,
0.96862745, 0.94509804, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.31372549, 0.61176471, 0.41960784, 0.99215686, 0.99215686,
0.80392157, 0.04313725, 0. , 0.16862745, 0.60392157,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.05490196,
0.00392157, 0.60392157, 0.99215686, 0.35294118, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.54509804,
0.99215686, 0.74509804, 0.00784314, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.04313725, 0.74509804, 0.99215686,
0.2745098 , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.1372549 , 0.94509804, 0.88235294, 0.62745098,
0.42352941, 0.00392157, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.31764706, 0.94117647, 0.99215686, 0.99215686, 0.46666667,
0.09803922, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.17647059,
0.72941176, 0.99215686, 0.99215686, 0.58823529, 0.10588235,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.0627451 , 0.36470588,
0.98823529, 0.99215686, 0.73333333, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.97647059, 0.99215686,
0.97647059, 0.25098039, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.18039216, 0.50980392,
0.71764706, 0.99215686, 0.99215686, 0.81176471, 0.00784314,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.15294118,
0.58039216, 0.89803922, 0.99215686, 0.99215686, 0.99215686,
0.98039216, 0.71372549, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.09411765, 0.44705882, 0.86666667, 0.99215686, 0.99215686,
0.99215686, 0.99215686, 0.78823529, 0.30588235, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.09019608, 0.25882353, 0.83529412, 0.99215686,
0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.31764706,
0.00784314, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.07058824, 0.67058824, 0.85882353,
0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.76470588,
0.31372549, 0.03529412, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.21568627, 0.6745098 ,
0.88627451, 0.99215686, 0.99215686, 0.99215686, 0.99215686,
0.95686275, 0.52156863, 0.04313725, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.53333333, 0.99215686, 0.99215686, 0.99215686,
0.83137255, 0.52941176, 0.51764706, 0.0627451 , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. ])
继续执行：
y_train = keras.utils.to_categorical(y_train,10) y_test = keras.utils.to_categorical(y_test,10)
继续执行：
model = Sequential() # 构建一个空的序贯模型 # 添加神经网络层 model.add(Dense(512,activation='relu',input_shape=(784,))) model.add(Dense(256,activation='relu')) model.add(Dense(10,activation='softmax')) model.summary()
返回信息：
WARNING:tensorflow:From C:\3rd\Anaconda2\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 512) 401920 _________________________________________________________________ dense_2 (Dense) (None, 256) 131328 _________________________________________________________________ dense_3 (Dense) (None, 10) 2570 ================================================================= Total params: 535,818 Trainable params: 535,818 Non-trainable params: 0 _________________________________________________________________
继续执行：
model.compile(optimizer=SGD(),loss='categorical_crossentropy',metrics=['accuracy']) model.fit(x_train,y_train,batch_size=64,epochs=5,validation_data=(x_test,y_test)) # 此处直接将测试集用作了验证集
返回正在执行信息：
执行结束信息：
WARNING:tensorflow:From C:\3rd\Anaconda2\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. Train on 60000 samples, validate on 10000 samples Epoch 1/5 60000/60000 [==============================] - 7s 123us/step - loss: 0.7558 - acc: 0.8162 - val_loss: 0.3672 - val_acc: 0.8991 Epoch 2/5 60000/60000 [==============================] - 7s 112us/step - loss: 0.3356 - acc: 0.9068 - val_loss: 0.2871 - val_acc: 0.9204 Epoch 3/5 60000/60000 [==============================] - 7s 112us/step - loss: 0.2798 - acc: 0.9211 - val_loss: 0.2537 - val_acc: 0.9296 Epoch 4/5 60000/60000 [==============================] - 7s 117us/step - loss: 0.2468 - acc: 0.9302 - val_loss: 0.2313 - val_acc: 0.9332 Epoch 5/5 60000/60000 [==============================] - 7s 122us/step - loss: 0.2228 - acc: 0.9378 - val_loss: 0.2084 - val_acc: 0.9404 <keras.callbacks.History at 0x1dcaea054a8>
继续执行：
score = model.evaluate(x_test,y_test) #10000/10000 [==============================] - 1s 53us/step
继续执行：
print("loss:",score[0])
#loss: 0.2084256855905056
继续执行：
print("accu:",score[1])
#accu: 0.9404
参考：
https://www.cnblogs.com/ncuhwxiong/p/9836648.html
https://www.cnblogs.com/shinny/p/9283372.html
https://blog.csdn.net/qq_41096996/article/details/85947560
Keras入门——（1）全连接神经网络FCN的更多相关文章
- 深度学习tensorflow实战笔记（1）全连接神经网络（FCN）训练自己的数据（从txt文件中读取）
1.准备数据 把数据放进txt文件中(数据量大的话,就写一段程序自己把数据自动的写入txt文件中,任何语言都能实现),数据之间用逗号隔开,最后一列标注数据的标签(用于分类),比如0,1.每一行表示一个 ...
- TensorFlow之DNN（二）：全连接神经网络的加速技巧(Xavier初始化、Adam、Batch Norm、学习率衰减与梯度截断)
在上一篇博客<TensorFlow之DNN(一):构建“裸机版”全连接神经网络>中,我整理了一个用TensorFlow实现的简单全连接神经网络模型,没有运用加速技巧(小批量梯度下降不算哦) ...
- TensorFlow之DNN（一）：构建“裸机版”全连接神经网络
博客断更了一周,干啥去了?想做个聊天机器人出来,去看教程了,然后大受打击,哭着回来补TensorFlow和自然语言处理的基础了.本来如意算盘打得挺响,作为一个初学者,直接看项目(不是指MINIST手写 ...
- MINIST深度学习识别：python全连接神经网络和pytorch LeNet CNN网络训练实现及比较（三）
版权声明:本文为博主原创文章,欢迎转载,并请注明出处.联系方式:460356155@qq.com 在前两篇文章MINIST深度学习识别:python全连接神经网络和pytorch LeNet CNN网 ...
- tensorflow中使用mnist数据集训练全连接神经网络-学习笔记
tensorflow中使用mnist数据集训练全连接神经网络 ——学习曹健老师“人工智能实践:tensorflow笔记”的学习笔记, 感谢曹老师 前期准备:mnist数据集下载,并存入data目录: ...
- 【TensorFlow/简单网络】MNIST数据集-softmax、全连接神经网络，卷积神经网络模型
初学tensorflow,参考了以下几篇博客: soft模型 tensorflow构建全连接神经网络 tensorflow构建卷积神经网络 tensorflow构建卷积神经网络 tensorflow构 ...
- 全卷积神经网络FCN理解
论文地址:https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf 这篇论文使用全卷积神经网络来做语义上的图像分割,开创了这一领 ...
- 如何使用numpy实现一个全连接神经网络？（上）
全连接神经网络的概念我就不介绍了,对这个不是很了解的朋友,可以移步其他博主的关于神经网络的文章,这里只介绍我使用基本工具实现全连接神经网络的方法. 所用工具: numpy == 1.16.4 matp ...
- Tensorflow 多层全连接神经网络
本节涉及: 身份证问题 单层网络的模型 多层全连接神经网络 激活函数 tanh 身份证问题新模型的代码实现 模型的优化 一.身份证问题 身份证号码是18位的数字[此处暂不考虑字母的情况],身份证倒数第 ...
随机推荐
- dbGet (二）
dbGet是由它基本的语法加上各种object的attribute的组合构成的.大家在熟悉基本语法之后,就应该去学习各个object的attribute了.说实话,这很难,因为attribute很多, ...
- 迭代器:遍历集合元素的操作. iterator()
package seday11; import java.util.ArrayList;import java.util.Collection;import java.util.Iterator; / ...
- ubuntu16.04spyder闪退
解决办法我试了好用 sudo pip install --upgrade html5lib==.0b8 完事.
- Java - 判断字符串是否是回文
首先,回文是指类似于“12345”,“abcdcba”的形式,即正念和反念都是一样的字符串 判断字符串是否是回文,这边介绍3种办法 将字符串翻转,判断翻转后的字符串和原字符串是否相等 public s ...
- element中的分页
在template <template> <div class="Terminal" v-loading="loading"> < ...
- 设备驱动基础学习--/proc下增加节点
在需要创建一个由一系列数据顺序组合而成的/proc虚拟文件或一个较大的/proc虚拟文件时,推荐使用seq_file接口. 数据结构struct seq_fille定义在include/linux/s ...
- 那些年做过的ctf之加密篇（加强版）
MarkdownPad Document *:first-child { margin-top: 0 !important; } body>*:last-child { margin-botto ...
- Linux下mysql使用systemctl restart mysqld命令失败
Linux下mysql使用systemctl restart mysqld命令失败: 解决方法:将mysql赋予root的权限 vim /etc/passwd 找到mysql开头的一行文件mysql: ...
- 试题编号： 201903-3 试题名称： 损坏的RAID5
这题的数据未免也太水了,题目的意思好像默认是每块磁盘装载数据的长度是相等的.我写了判断每次取数据是否会超过每块磁盘存的数据的长度,然而并没有什么卵用.交上去20分,写了个数据测了下,如果要求的块太大的 ...
- 【代码学习】PYTHON 生成器
一.生成器 一遍循环一遍计算的机制,称为生成器 二.生成器的特点: 1.节约内存 2.迭代到下一次的调用时,所使用的参数都是第一次所保留下的,即是说,在整个所有函数调用的参数都是第一次所调用时保留的, ...