keras 全连接手写识别 coursera week2

Exercise 2
In the course you learned how to do classificaiton using Fashion MNIST, a data set containing items of clothing. There’s another, similar dataset called MNIST which has items of handwriting – the digits 0 through 9.

Write an MNIST classifier that trains to 99% accuracy or above, and does it without a fixed number of epochs – i.e. you should stop training once you reach that level of accuracy.

Some notes:

1. It should succeed in less than 10 epochs, so it is okay to change epochs= to 10, but nothing larger
2. When it reaches 99% or greater it should print out the string “Reached 99% accuracy so cancelling training!”
3. If you add any additional variables, make sure you use the same names as the ones used in the class

I’ve started the code for you below – how would you finish it?

1.全连接手写识别

import tensorflow as tf from os import path, getcwd, chdir # DO NOT CHANGE THE LINE BELOW. If you are developing in a local # environment, then grab mnist.npz from the Coursera Jupyter Notebook # and place it inside a local folder and edit the path to that location path = f"{getcwd()}/../tmp2/mnist.npz"

# GRADED FUNCTION: train_mnist def train_mnist(): # Please write your code only where you are indicated. # please do not remove # model fitting inline comments. # YOUR CODE SHOULD START HERE **class myCallback(tf.keras.callbacks.Callback): def on_epoch_end(self,epoch,logs={}): if(logs.get("acc")>0.6): print("\nReached 60% accuracy so cancelling training!") self.model.stop_training=True** # YOUR CODE SHOULD END HERE mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data(path=path) # YOUR CODE SHOULD START HERE **callbacks=myCallback()** # YOUR CODE SHOULD END HERE model = tf.keras.models.Sequential([ # YOUR CODE SHOULD START HERE **tf.keras.layers.Flatten(input_shape=(28,28)), tf.keras.layers.Dense(512,activation='relu'), tf.keras.layers.Dense(10,activation='softmax')** # YOUR CODE SHOULD END HERE ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # model fitting history = model.fit(# YOUR CODE SHOULD START HERE **x_train,y_train,epochs=10,callbacks=[callbacks]** # YOUR CODE SHOULD END HERE ) # model fitting return history.epoch, history.history['acc'][-1]

train_mnist()

2.卷积手写识别
改几处地方就行了

首先 卷积要求图片是3维 mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data(path=path) x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1) x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1)

改模型层 改为卷积 model = tf.keras.models.Sequential([ # YOUR CODE SHOULD START HERE # tf.keras.layers.Flatten(input_shape=(28,28)), # tf.keras.layers.Dense(512,activation='relu'), # tf.keras.layers.Dense(10,activation='softmax') tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)), tf.keras.layers.MaxPooling2D(2,2), tf.keras.layers.Conv2D(64,(3,3),activation='relu'), tf.keras.layers.MaxPooling2D(2,2), tf.keras.layers.Flatten(), tf.keras.layers.Dense(10,activation='softmax') # YOUR CODE SHOULD END HERE ])

训练的时候加入测试集作为验证 history = model.fit(# YOUR CODE SHOULD START HERE x_train,y_train,epochs=10,callbacks=[callbacks], # YOUR CODE SHOULD END HERE validation_data=(x_test, y_test) )

如果上面没有validation_data=(x_test, y_test)，在 model.fit 外面加上 **test_loss = model.evaluate(x_test, y_test)** 也是可以验证的

损失函数说明

如果你的 targets 是 one-hot 编码，用 categorical_crossentropy
one-hot 编码：[0, 0, 1], [1, 0, 0], [0, 1, 0]
如果你的 tagets 是 数字编码 ，用 sparse_categorical_crossentropy
数字编码：2, 0, 1