之前一直在用tensorflow，被同學推薦來用keras了，把之前文件中的mnist手寫數字資料集拿來練手，程式碼如下。

import struct
import numpy as np
import os

import keras
from keras.models import Sequential  
from keras.layers import Dense
from keras.optimizers import SGD

def load_mnist(path, kind):
    labels_path = os.path.join(path, '%s-labels.idx1-ubyte' % kind)
    images_path = os.path.join(path, '%s-images.idx3-ubyte' % kind)
    with open(labels_path, 'rb') as lbpath:
        magic, n = struct.unpack('>II', lbpath.read(8))
        labels = np.fromfile(lbpath, dtype=np.uint8)
    with open(images_path, 'rb') as imgpath:
        magic, num, rows, cols = struct.unpack(">IIII", imgpath.read(16))
        images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)  #28*28=784
    return images, labels

#loading train and test data
X_train, Y_train = load_mnist('.\\data', kind='train')
X_test, Y_test = load_mnist('.\\data', kind='t10k')

#turn labels to one_hot code
Y_train_ohe = keras.utils.to_categorical(Y_train, num_classes=10)

#define models
model = Sequential()

model.add(Dense(input_dim=X_train.shape[1],output_dim=50,init='uniform',activation='tanh'))
model.add(Dense(input_dim=50,output_dim=50,init='uniform',activation='tanh'))
model.add(Dense(input_dim=50,output_dim=Y_train_ohe.shape[1],init='uniform',activation='softmax')) 

sgd = SGD(lr=0.001, decay=1e-7, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=["accuracy"])

#start training
model.fit(X_train,Y_train_ohe,epochs=50,batch_size=300,shuffle=True,verbose=1,validation_split=0.3)

#count accuracy
y_train_pred = model.predict_classes(X_train, verbose=0)
  
train_acc = np.sum(Y_train == y_train_pred, axis=0) / X_train.shape[0]  
print('Training accuracy: %.2f%%' % (train_acc * 100))
  
y_test_pred = model.predict_classes(X_test, verbose=0)
test_acc = np.sum(Y_test == y_test_pred, axis=0) / X_test.shape[0]  
print('Test accuracy: %.2f%%' % (test_acc * 100))
 

訓練結果如下：

Epoch 45/50
42000/42000 [==============================] - 1s 17us/step - loss: 0.2174 - acc: 0.9380 - val_loss: 0.2341 - val_acc: 0.9323
Epoch 46/50
42000/42000 [==============================] - 1s 17us/step - loss: 0.2061 - acc: 0.9404 - val_loss: 0.2244 - val_acc: 0.9358
Epoch 47/50
42000/42000 [==============================] - 1s 17us/step - loss: 0.1994 - acc: 0.9413 - val_loss: 0.2295 - val_acc: 0.9347
Epoch 48/50
42000/42000 [==============================] - 1s 17us/step - loss: 0.2003 - acc: 0.9413 - val_loss: 0.2224 - val_acc: 0.9350
Epoch 49/50
42000/42000 [==============================] - 1s 18us/step - loss: 0.2013 - acc: 0.9417 - val_loss: 0.2248 - val_acc: 0.9359
Epoch 50/50
42000/42000 [==============================] - 1s 17us/step - loss: 0.1960 - acc: 0.9433 - val_loss: 0.2300 - val_acc: 0.9346
Training accuracy: 94.11%
Test accuracy: 93.61%