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
- winform学习（11）Timer控件
利用Timer控件制作简单的跑马灯: 拉一个Lable控件至窗体中心,Text内容为★▶◀★▶◀★▶◀★▶◀ 再拉一个Timer控件,属性Enabled设置为True(即开启控件),Interval设 ...
- 每天进步一点点------Allegro 蛇形走线
对于高速数据总线,如果芯片内部没有延时调节功能,通常使用蛇形走线来调整延时以满足时序要求,也就是通常所说的等长线.蛇形走线的目的是调整延时,所以这一类网络都有延迟或相对延迟约束.所以在做蛇形走线调整时 ...
- MODULE BUILD FAILED: ERROR: COULDN’T FIND PRESET “ES2015” RELATIVE TO DIRECTORY
npm run dev 遇到报错: Module build failed: Error: Couldn't find preset "es2015" relative to di ...
- Docker on startup: “No activity detected on VM, aborting”
windows下安装的docker,切换到linux,一直处于重启中,最后报No activity detected on VM, aborting错误 上网百度一下这个错误,基本上没人遇到过,最后在 ...
- MongoDB-2 安装与配置
安装建议 MongoDB 可以在mac/win/linux上安装,我个人建议在linux上安装会更好,这样测试起来更方便.一般来说本地只装mysql或者MariaDB,其余的中间件一律放linux,让 ...
- Java-POJ1000-A+B Problem
- AcWing 852. spfa判断负环 边权可能为负数。
#include <cstring> #include <iostream> #include <algorithm> #include <queue> ...
- Jmeter注册100个账户的三个方法
Jmeter注册账户比如注册成千上万个账户,如何快速实现呢? 三种方法分别举例注册5个账户 1)添加CSV data config_txt 2)添加CSV data config_csv 3)函数助手 ...
- Go并发介绍
1. 进程.线程.协程 进程(Process),线程(Thread),协程(Coroutine,也叫轻量级线程) 进程 进程是一个程序在一个数据集中的一次动态执行过程,可以简单理解为“正在执行的程序” ...
- （爬虫）随机生成一个header
#!/usr/bin/env python #-*- coding: utf-8 -*- #__Author__: yunrui #__Version__: 1.0 #__Time__: 2019/1 ...