@[TOC](Coursera TensorFlow(Keras) 一步步手写体Fashion Mnist识别分类(2) Tensorflow和ML, DL 机器学习/深度学习Coursera教程分享 )
Fashion MNIST数据简介
相信很多人,对于Mnist这个数据集都已经学腻了。现在出了个Fashion Mnist更加有趣,而且tensorflow/keras自带这个数据集非常方便调用。这个数据集包含了很多衣服、鞋子的图片,每张图片恰好也是[28*28]的shape,很容易处理。
我们可以打印出一张看看,是一只鞋子。
模型构建
我们按照coursera上的教程直接上手写一个分类模型:
先下载数据
from tensorflow.examples.tutorials.mnist import input_datadata = input_data.read_data_sets('data/fashion', source_url='http://fashion-mnist.s3-website.eu-central-/')# 如果已经下载直接读入data = input_data.read_data_sets('data/fashion')BATCH_SIZE = 64# 可以检查下数据,没问题data.train.next_batch(BATCH_SIZE)
当然,keras里面已经有这个数据集了,直接load就行。下面开始写模型:
from tensorflow import kerasfashion_mnist = keras.datasets.fashion_mnist(X_train, Y_train), (X_test, Y_test) = fashion_mnist.load_data()# 输出看看X, Y的 shapeX_train.shape, X_test.shape, Y_train.shape, Y_test.shape# ((60000, 28, 28), (10000, 28, 28), (60000,), (10000,))
#simply normlizing the dataX_train = X_train / 255.X_test = X_test / 255.
import tensorflow as tffrom keras import Sequentialfrom keras.layers import Dense, Conv2D, MaxPool2D, Flattenmodel = Sequential()model.add(Flatten())model.add(Dense(128, activation='relu'))model.add(Dense(10, activation='softmax'))pile(tf.train.AdamOptimizer(), loss='sparse_categorical_crossentropy', metrics=['acc'])model.fit(X_train, Y_train, epochs=10, batch_size=32, validation_data=(X_test, Y_test))
Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 [] - 3s 48us/step - loss: 0.2165 - acc: 0.9197 - val_loss: 0.3426 - val_acc: 0.8861 Epoch 2/10 60000/60000 [
Epoch 3/10
60000/60000 [==============================] - 3s 47us/step - loss: 0.2074 - acc: 0.9215 - val_loss: 0.3464 - val_acc: 0.8814
模型效果
检查模型预测结果:
预测20条
preds = model.predict(X_test[:20])
preds.argmax(axis=-1)
预测结果:
array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7, 4, 5, 5, 3, 4, 1, 2, 2, 8, 0])
真是结果:
Y_test[:20]
array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7, 4, 5, 7, 3, 4, 1, 2, 4, 8, 0])
发现大多数都预测正确了。
在全部数据集上测试模型结果:
model.evaluate(X_test, Y_test)
loss和准确率accuracy分别如下:
[0.3542726508885622, 0.8834]
我们也可以增加一个keras的callback函数,每个epoch结束时调用
class myCallback(tf.keras.callbacks.Callback):def on_epoch_end(self, epoch, logs={}):if(logs.get('acc')>0.9):print("\nReached 90% accuracy so cancelling training!")# if(logs.get('loss')<0.15):# print("\nReached 0.15 loss so cancelling training!")self.model.stop_training = Truecallbacks = myCallback()
这样我们再次训练的时候,一旦准确率超过90%,训练就会停止。
model.fit(X_train, Y_train, epochs=10, batch_size=32, callbacks=[callbacks])
Epoch 1/10
60000/60000 [==============================] - 3s 45us/step - loss: 0.1320 - acc: 0.9498
Reached 90% accuracy so cancelling training!