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本脚本是训练keras 的mnist 数字识别程序 ，以前发过了 ，今天把 预测实现了，
# Larger CNN for the MNIST Dataset # 2.Negative dimension size caused by
subtracting 5 from 1 for 'conv2d_4/convolution' (op: 'Conv2D') with input
shapes # 3.UserWarning: Update your `Conv2D` call to the Keras 2 API:
http://blog.csdn.net/johinieli/article/details/69222956 # 4.Error when checking
input: expected conv2d_1_input to have shape (None, 28, 28, 1) but got array
with shape (60000, 1, 28, 28) # talk to wumi,you good . # python 3.5.4 #
keras.__version__ : 2.0.6 # thensorflow 1.2.1 # theano 0.10.0beta1 # good blog
# http://blog.csdn.net/shizhengxin123/article/details/72383728 #
http://www.360doc.com/content/17/0415/12/1489589_645772879.shtml # recommand
another framework http://tflearn.org/examples/ import numpy import keras from
keras.datasets import mnist from keras.models import Sequential from
keras.layers import Dense from keras.layers import Dropout from keras.layers
import Flatten from keras.layers.convolutional import Conv2D from
keras.layers.convolutional import MaxPooling2D from keras.utils import np_utils
import matplotlib.pyplot as plt from keras.constraints import maxnorm from
keras.optimizers import SGD from keras.preprocessing import image import
skimage.io # fix random seed for reproducibility seed = 7
numpy.random.seed(seed) # load data (X_train, y_train), (X_test, y_test) =
mnist.load_data() plt.subplot(221) plt.imshow(X_train[0],
cmap=plt.get_cmap('gray')) plt.show() # reshape to be
[samples][pixels][width][height] X_train = X_train.reshape(X_train.shape[0],
28, 28, 1).astype('float32') X_test = X_test.reshape(X_test.shape[0], 28, 28,
1).astype('float32') # X_train = X_train.reshape(1, 28, 28,
1).astype('float32') ValueError: cannot reshape array of size 47040000 into
shape (1,28,28,1) #X_test = X_test.reshape(1, 28, 28, 1).astype('float32')
ValueError: cannot reshape array of size 47040000 into shape (1,28,28,1) #
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32') #
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32') <---4 #
normalize inputs from 0-255 to 0-1 X_train = X_train / 255 X_test = X_test /
255 # one hot encode outputs y_train = np_utils.to_categorical(y_train) y_test
= np_utils.to_categorical(y_test) num_classes = y_test.shape[1] ###raw # define
the larger model def larger_model(): # create model model = Sequential()
model.add(Conv2D(30, (5, 5), padding='valid', input_shape=(28, 28, 1),
activation='relu')) # model.add(Conv2D(30, (5, 5), padding='valid',
input_shape=(28, 28,1), activation='relu')) <----3,2
model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.4))
model.add(Conv2D(15, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.4))
model.add(Flatten()) model.add(Dense(128, activation='relu'))
model.add(Dropout(0.4)) model.add(Dense(50, activation='relu'))
model.add(Dropout(0.4)) model.add(Dense(num_classes, activation='softmax')) #
Compile model # optimizer 优化器 # loss 损失函数
model.compile(loss='categorical_crossentropy', optimizer='adam',
metrics=['accuracy']) return model # build the model model = larger_model() #
Fit the model #
fit函数返回一个History的对象，其History.history属性记录了损失函数和其他指标的数值随epoch变化的情况，如果有验证集的话，也包含了验证集的这些指标变化情况
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10,
batch_size=200, verbose=2) # epochs 200 too bigger # model.fit(X_train,
y_train, validation_data=(X_test, y_test), nb_epoch=200, batch_size=200,
verbose=2) # Final evaluation of the model scores = model.evaluate(X_test,
y_test, verbose=0) print("Large CNN Error: %.2f%%" % (100 - scores[1] * 100)) #
save the model
model.save('D:\\works\\jetBrians\\PycharmProjects\\tryPicture\\my_model.h5') #
creates a HDF5 file 'my_model.h5' del model # reload the modle # returns a
compiled model # identical to the previous one # modelTrained = Sequential() #
model =
modelTrained.load_model('D:\\works\\jetBrians\\PycharmProjects\\tryPicture\\my_model.h5')
# https://gist.github.com/ageitgey/a40dded08e82e59724c70da23786bbf0 # write a
number in a picture # predict numbers #image_path = './lena.jpg' # method 1 #
load pic #img = image.load_img(image_path, target_size=(28, 28)) # handle pic
#x = image.img_to_array(img) #x = numpy.expand_dims(x, axis=0) #x =
preprocess_input(x) # method2 #img2 = skimage.io.imread(image_path,
as_grey=True) #skimage.io.imshow(img2) #plt.show() #img2 = numpy.reshape(img2,
(1, 28, 28, 1)).astype('float32') # 对数字进行预测
#https://baijiahao.baidu.com/s?id=1574962680356106&wfr=spider&for=pc #predict =
model.predict(img2, verbose=0) #result = model.prediect_classes(img2,
verbose=0) #print(predict[0]) #print(result[0]) #some warning tips The
TensorFlow library wasn't compiled to use AVX2 instructions, but these are
available on your machine and could speed up CPU computations. #have no idea
what's the meaning 原来数据样式 =================训练log
D:\applications\Anaconda3\python.exe
D:/works/jetBrians/PycharmProjects/tryPicture/trainModel/TrainModel.py Using
TensorFlow backend. Train on 60000 samples, validate on 10000 samples Epoch
1/10 62s - loss: 0.8830 - acc: 0.7027 - val_loss: 0.1566 - val_acc: 0.9545
Epoch 2/10 56s - loss: 0.3130 - acc: 0.9078 - val_loss: 0.0955 - val_acc:
0.9712 Epoch 3/10 61s - loss: 0.2342 - acc: 0.9340 - val_loss: 0.0737 -
val_acc: 0.9763 Epoch 4/10 58s - loss: 0.1924 - acc: 0.9458 - val_loss: 0.0643
- val_acc: 0.9802 Epoch 5/10 60s - loss: 0.1678 - acc: 0.9534 - val_loss:
0.0541 - val_acc: 0.9848 Epoch 6/10 53s - loss: 0.1541 - acc: 0.9578 -
val_loss: 0.0468 - val_acc: 0.9849 Epoch 7/10 53s - loss: 0.1396 - acc: 0.9617
- val_loss: 0.0464 - val_acc: 0.9852 Epoch 8/10 55s - loss: 0.1303 - acc:
0.9647 - val_loss: 0.0422 - val_acc: 0.9871 Epoch 9/10 52s - loss: 0.1276 -
acc: 0.9656 - val_loss: 0.0398 - val_acc: 0.9871 Epoch 10/10 53s - loss: 0.1156
- acc: 0.9680 - val_loss: 0.0370 - val_acc: 0.9876 Large CNN Error: 1.24%
Process finished with exit code 0
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