DNN識別mnist手寫數字

提取碼：sg3f

導庫

import numpy as np

import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

import numpy as np

path='./mnist.npz'
f = np.load(path)
train_x, train_y = f['x_train'], f['y_train']    # 訓練集
test_x, test_y = f['x_test'], f['y_test']    # 測試集
f.close()

將資料以形式輸出

將資料格式改為dnn可接收的一維格式

train_x = train_x.reshape((60000,28*28),order='c')    # 將二維的展開為一維的資料(訓練集)  

test_x = test_x.reshape((10000,28*28),order='c')    # 將二維的展開為一維的資料（測試集）

model = keras.sequential()

model.add(layers.dense(100,activation='relu',input_dim=28*28))
model.add(layers.dense(10,activation='softmax'))
adam = keras.optimizers.adam(lr=0.01)
model.compile(optimizer=adam,loss='sparse_categorical_crossentropy',metrics=['acc'])
model.fit(train_x,train_y,epochs=50,batch_size=512)

dnn在測試集上的loss和準確率如下

model.evaluate(test_x,test_y)

import numpy as np

import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
path='./mnist.npz'
f = np.load(path)
train_x, train_y = f['x_train'], f['y_train']    # 訓練集
test_x, test_y = f['x_test'], f['y_test']    # 測試集
f.close()
print(train_x.shape)
print(train_y.shape)
print(test_x.shape)
print(test_y.shape)
plt.imshow(train_x[10000])
train_x = train_x.reshape((60000,28*28),order='c')    # 將二維的展開為一維的資料(訓練集)  
test_x = test_x.reshape((10000,28*28),order='c')    # 將二維的展開為一維的資料（測試集）
model = keras.sequential()
model.add(layers.dense(100,activation='relu',input_dim=28*28))
model.add(layers.dense(10,activation='softmax'))
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['acc'])
model.fit(train_x,train_y,epochs=50,batch_size=512)
model.evaluate(test_x,test_y)

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