tensorflow 的 MNIST 資料及共有訓練資料 55000 筆，驗證資料 5000筆，每一筆資料由 feature (影像)與 label (數字) 組成。
注意：要改用 tensorflow.keras.datasets
tensorflow.examples.tutorials is now deprecated and it is recommended to use tensorflow.keras.datasetsMNIST 資料處理
import tensorflow as tf
import numpy as np
mnist = tf.keras.datasets.mnist
# Tuple of Numpy arrays: (x_train, y_train), (x_test, y_test)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 將每個 pixel 的值從 Int 轉成 floating point 同時做normalize(這是很常見的preprocessing)
# x_train, x_test = x_train / 255.0, x_test / 255.0
# 查看 train Data
print('x_train length = ', len(x_train), ', x_test length = ', len(x_test))
# x_train length = 60000 , x_test length = 10000
print('x_train.shape = ', x_train.shape, ', x_train[0].shape = ', x_train[0].shape, ', x_test[0].shape=', x_test[0].shape)
# 每張圖片 大小為 28x28
# x_train.shape = (60000, 28, 28) , x_train[0].shape = (28, 28) , x_test[0].shape= (28, 28)
print('x_train[0].length = ', len(x_train[0]) )
# x_train[0].length = 28
print('y_train length = ', len(y_train), ', y_train[0] = ', y_train[0])
# y_train length = 60000 , y_train[0] = 5
# 將 第一張 x_train 的圖片儲存到檔案
import matplotlib.pyplot as plt
def plot_image(image, filename):
plt.clf()
plt.imshow(image.reshape(28,28), cmap='binary')
plt.savefig(filename)
plot_image(x_train[0], "x_train_index_0.png")
# 將 training 的 input 資料 28*28 的 2維陣列 轉為 1維陣列，再轉成 float32
# 每一個圖片，都變成 784 個 float 的 array
# training 與 testing 資料數量分別是 60000 與 10000 筆
# X_train_2D 是 [60000, 28*28] 的 2維陣列
x_train_2D = x_train.reshape(60000, 28*28).astype('float32')
x_test_2D = x_test.reshape(10000, 28*28).astype('float32')
print('x_train_2D.shape=', x_train_2D.shape)
# x_train_2D.shape=(60000, 784)
# 將圖片的數字 (0~255) 標準化，最簡單的方法就是直接除以 255
# x_train_norm 是標準化後的結果，每一個數字介於 0~1 之間
x_train_norm = x_train_2D/255
x_test_norm = x_test_2D/255
# 將 training 的 label 進行 one-hot encoding，例如數字 7 經過 One-hot encoding 轉換後是 array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)，即第7個值為 1
one_hot=tf.one_hot(y_train,10)
with tf.compat.v1.Session() as sess:
init = tf.compat.v1.global_variables_initializer()
sess.run(init)
y_train_one_hot = sess.run(one_hot)
print( y_train_one_hot )
print( 'y_train[0] = ', y_train[0], ", y_train_one_hot[0]=", y_train_one_hot[0] )
# y_train[0] = 5 , y_train_one_hot[0]= [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
# 也可以用 np.argmax 將 one_hot 陣列，轉換回原本的數字
print( 'y_train[0] = ', np.argmax(y_train_one_hot[0]) )
# y_train[0] = 5
# 列印前10筆 one hot
for i in range(10):
print(y_train_one_hot[i])
# [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
# [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
# [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
# [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
# [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
# [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
# [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
# [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
# [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
用一個 function 列印多筆圖片, label 資訊
import tensorflow as tf
import numpy as np
mnist = tf.keras.datasets.mnist
# Tuple of Numpy arrays: (x_train, y_train), (x_test, y_test)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 將 training 的 input 資料 28*28 的 2維陣列 轉為 1維陣列，再轉成 float32
# 每一個圖片，都變成 784 個 float 的 array
# training 與 testing 資料數量分別是 60000 與 10000 筆
# X_train_2D 是 [60000, 28*28] 的 2維陣列
x_train_2D = x_train.reshape(60000, 28*28).astype('float32')
x_test_2D = x_test.reshape(10000, 28*28).astype('float32')
print('x_train_2D.shape=', x_train_2D.shape)
# x_train_2D.shape=(60000, 784)
# 將圖片的數字 (0~255) 標準化，最簡單的方法就是直接除以 255
# x_train_norm 是標準化後的結果，每一個數字介於 0~1 之間
x_train_norm = x_train_2D/255
x_test_norm = x_test_2D/255
# 將 training 的 label 進行 one-hot encoding，例如數字 7 經過 One-hot encoding 轉換後是 array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)，即第7個值為 1
y_train_one_hot_tf=tf.one_hot(y_train,10)
y_test_one_hot_tf=tf.one_hot(y_test,10)
with tf.compat.v1.Session() as sess:
init = tf.compat.v1.global_variables_initializer()
sess.run(init)
y_train_one_hot = sess.run(y_train_one_hot_tf)
y_test_one_hot = sess.run(y_test_one_hot_tf)
# 查看多筆資料，以及 label
import matplotlib.pyplot as plt
def plot_images_labels_prediction(images,labels,prediction,idx,filename, num=10):
fig = plt.gcf()
fig.set_size_inches(12, 14)
if num>25: num=25
for i in range(0, num):
ax=plt.subplot(5,5, 1+i)
# 將 images 的 784 個數字轉換為 28x28
ax.imshow(np.reshape(images[idx],(28, 28)), cmap='binary')
# 轉換 one_hot label 為數字
title= "label=" +str(np.argmax(labels[idx]))
if len(prediction)>0:
title+=",predict="+str(prediction[idx])
ax.set_title(title,fontsize=10)
ax.set_xticks([]);ax.set_yticks([])
idx+=1
plt.savefig(filename)
plot_images_labels_prediction(x_train_2D, y_train_one_hot,[], 0, 'x_train_0.png', 10)
plot_images_labels_prediction(x_train_2D, y_train_one_hot,[], 10, 'x_train_1.png', 10)
plot_images_labels_prediction(x_test_2D, y_test_one_hot,[], 0, 'x_test_0.png', 10)
plot_images_labels_prediction(x_test_2D, y_test_one_hot,[], 10, 'x_test_1.png', 10)以 tensorflow 建立 MLP
訓練部分資料共有 60000 筆，經過預處理後，會產生 feature, label，然後輸入 MLP model 進行訓練，訓練完成的模型，可在預測階段使用。
import tensorflow as tf
import numpy as np
# STEP 1 讀取資料
mnist = tf.keras.datasets.mnist
# Tuple of Numpy arrays: (x_train, y_train), (x_test, y_test)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 將 training 的 input 資料 28*28 的 2維陣列 轉為 1維陣列，再轉成 float32
# 每一個圖片，都變成 784 個 float 的 array
# training 與 testing 資料數量分別是 60000 與 10000 筆
# X_train_2D 是 [60000, 28*28] 的 2維陣列
x_train_2D = x_train.reshape(60000, 28*28).astype('float32')
x_test_2D = x_test.reshape(10000, 28*28).astype('float32')
print('x_train_2D.shape=', x_train_2D.shape)
# x_train_2D.shape=(60000, 784)
# 將圖片的數字 (0~255) 標準化，最簡單的方法就是直接除以 255
# x_train_norm 是標準化後的結果，每一個數字介於 0~1 之間
x_train_norm = x_train_2D/255
x_test_norm = x_test_2D/255
# 將 training 的 label 進行 one-hot encoding，例如數字 7 經過 One-hot encoding 轉換後是 array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)，即第7個值為 1
y_train_one_hot_tf=tf.one_hot(y_train,10)
y_test_one_hot_tf=tf.one_hot(y_test,10)
y_train_one_hot = None
y_test_one_hot = None
with tf.compat.v1.Session() as sess:
init = tf.compat.v1.global_variables_initializer()
sess.run(init)
y_train_one_hot = sess.run(y_train_one_hot_tf)
y_test_one_hot = sess.run(y_test_one_hot_tf)
# 將 x_train, y_train 分成 train 與 validation 兩個部分
x_train_norm_data = x_train_norm[0:50000]
x_train_norm_validation = x_train_norm[50000:60000]
y_train_one_hot_data = y_train_one_hot[0:50000]
y_train_one_hot_validation = y_train_one_hot[50000:60000]
### 建立模型
# keras 只需要用 model = Sequential() 建立線性堆疊模型，再用 model.add() 將各神經網路層加入模型，但 tensorflow 需要自己定義 layer 函數
def layer(output_dim,input_dim,inputs, activation=None):
W = tf.Variable(tf.random.normal([input_dim, output_dim]))
b = tf.Variable(tf.random.normal([1, output_dim]))
XWb = tf.matmul(inputs, W) + b
if activation is None:
outputs = XWb
else:
outputs = activation(XWb)
return outputs
# 輸入層, 784 個神經元, 資料型別為 float
# 第一維是 None，因為輸入資料的筆數還不確定，所以設定為 None
# 第二維是 784，因為每個圖片是 784 個像素點
x = tf.compat.v1.placeholder("float", [None, 784])
# 隱藏層, 256 個神經元
h1=layer(output_dim=256,input_dim=784, inputs=x ,activation=tf.nn.relu)
# 輸出層, 10 個神經元
y_predict=layer(output_dim=10,input_dim=256, inputs=h1,activation=None)
#### 定義訓練方式
# keras 要用 model.compile，設定 loss function 及 optimizer 與 metrics 設定評估模型的方法
# tensorflow 需要自己定義 loass function、最優化方法 optimizer，及設定參數，以及定義評估模型準確率的公式
# 建立訓練資料 label 真實值 placeholder
y_label = tf.compat.v1.placeholder("float", [None, 10])
# 定義loss function
loss_function = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits
(logits=y_predict ,
labels=y_label))
# 選擇optimizer
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=0.001).minimize(loss_function)
### 定義評估模型的準確率
#計算每一筆資料是否正確預測
correct_prediction = tf.equal(tf.argmax(y_label , 1),
tf.argmax(y_predict, 1))
#將計算預測正確結果，加總平均
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
### 訓練模型
trainEpochs = 15
batchSize = 100
totalBatchs = int(len(x_train_norm_data)/batchSize)
epoch_list=[];loss_list=[];accuracy_list=[]
from time import time
with tf.compat.v1.Session() as sess:
startTime=time()
sess.run(tf.compat.v1.global_variables_initializer())
for epoch in range(trainEpochs):
for i in range(totalBatchs):
# batch_x, batch_y = mnist.train.next_batch(batchSize)
batch_x = x_train_norm_data[i*batchSize:(i+1)*batchSize]
batch_y = y_train_one_hot_data[i*batchSize:(i+1)*batchSize]
sess.run(optimizer,feed_dict={x: batch_x,y_label: batch_y})
# loss,acc = sess.run([loss_function,accuracy],
# feed_dict={x: mnist.validation.images,
# y_label: mnist.validation.labels})
loss,acc = sess.run([loss_function,accuracy],
feed_dict={x: x_train_norm_validation,
y_label: y_train_one_hot_validation})
epoch_list.append(epoch);loss_list.append(loss)
accuracy_list.append(acc)
print("Train Epoch:", '%02d' % (epoch+1), "Loss=", "{:.9f}".format(loss)," Accuracy=",acc)
duration =time()-startTime
print("Train Finished takes:",duration)
### 評估模型準確率
print("Accuracy:", sess.run(accuracy,
feed_dict={x: x_test_norm,
y_label: y_test_one_hot}))
### 進行預測
prediction_result=sess.run(tf.argmax(y_predict,1),
feed_dict={x: x_test_norm })
# matplotlib 列印 loss, accuracy 折線圖
import matplotlib.pyplot as plt
fig = plt.gcf()
# fig.set_size_inches(4,2)
plt.plot(epoch_list, loss_list, label = 'loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['loss'], loc='upper left')
plt.savefig('loss.png')
fig = plt.gcf()
# fig.set_size_inches(4,2)
plt.plot(epoch_list, accuracy_list,label="accuracy" )
plt.ylim(0.8,1)
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['accuracy'], loc='upper right')
plt.savefig('accuracy.png')
############
# 查看多筆資料，以及 label
import matplotlib.pyplot as plt
def plot_images_labels_prediction(images,labels,prediction,idx,filename, num=10):
fig = plt.gcf()
fig.set_size_inches(12, 14)
if num>25: num=25
for i in range(0, num):
ax=plt.subplot(5,5, 1+i)
# 將 images 的 784 個數字轉換為 28x28
ax.imshow(np.reshape(images[idx],(28, 28)), cmap='binary')
# 轉換 one_hot label 為數字
title= "label=" +str(np.argmax(labels[idx]))
if len(prediction)>0:
title+=",predict="+str(prediction[idx])
ax.set_title(title,fontsize=10)
ax.set_xticks([]);ax.set_yticks([])
idx+=1
plt.savefig(filename)
plot_images_labels_prediction(x_test_norm,
y_test_one_hot,
prediction_result,0, "result.png", num=10)
# 找出預測錯誤
for i in range(400):
if prediction_result[i]!=np.argmax(y_test_one_hot[i]):
print("i="+str(i)+
" label=",np.argmax(y_test_one_hot[i]),
"predict=",prediction_result[i])
Train Epoch: 01 Loss= 6.828331470 Accuracy= 0.8309
Train Epoch: 02 Loss= 4.488496780 Accuracy= 0.8758
Train Epoch: 03 Loss= 3.577744246 Accuracy= 0.8964
Train Epoch: 04 Loss= 3.057111502 Accuracy= 0.9064
Train Epoch: 05 Loss= 2.736637831 Accuracy= 0.9137
Train Epoch: 06 Loss= 2.479548931 Accuracy= 0.9204
Train Epoch: 07 Loss= 2.278975010 Accuracy= 0.9226
Train Epoch: 08 Loss= 2.162630558 Accuracy= 0.9254
Train Epoch: 09 Loss= 2.005532503 Accuracy= 0.9295
Train Epoch: 10 Loss= 1.911731601 Accuracy= 0.9324
Train Epoch: 11 Loss= 1.785833955 Accuracy= 0.9351
Train Epoch: 12 Loss= 1.694522023 Accuracy= 0.9371
Train Epoch: 13 Loss= 1.660093784 Accuracy= 0.9362
Train Epoch: 14 Loss= 1.623517632 Accuracy= 0.938
Train Epoch: 15 Loss= 1.616030455 Accuracy= 0.9384
Train Finished takes: 36.676905393600464
Accuracy: 0.9361
## 預測錯誤的圖片
i=8 label= 5 predict= 6
i=33 label= 4 predict= 0
i=41 label= 7 predict= 3
i=59 label= 5 predict= 8
i=63 label= 3 predict= 2
i=78 label= 9 predict= 7
i=115 label= 4 predict= 6
i=121 label= 4 predict= 8
i=126 label= 0 predict= 2
i=175 label= 7 predict= 2
i=215 label= 0 predict= 2
i=241 label= 9 predict= 8
i=247 label= 4 predict= 2
i=282 label= 7 predict= 8
i=290 label= 8 predict= 4
i=320 label= 9 predict= 8
i=321 label= 2 predict= 8
i=324 label= 0 predict= 8
i=325 label= 4 predict= 9
i=333 label= 5 predict= 3
i=359 label= 9 predict= 4
i=389 label= 9 predict= 4
將隱藏層的神經元由 256 改為 1000
# 隱藏層, 1000 個神經元
h1=layer(output_dim=1000,input_dim=784, inputs=x ,activation=tf.nn.relu)
# 輸出層, 10 個神經元
y_predict=layer(output_dim=10,input_dim=1000, inputs=h1,activation=None)剛剛的正確率為
Accuracy: 0.9361改為 1000 後的正確率提升為
Accuracy: 0.95建立兩個隱藏層
x = tf.compat.v1.placeholder("float", [None, 784])
# 隱藏層 h1, 1000 個神經元
h1=layer(output_dim=1000,input_dim=784, inputs=x ,activation=tf.nn.relu)
# 隱藏層 h2, 1000 個神經元
h2=layer(output_dim=1000,input_dim=1000, inputs=h1 ,activation=tf.nn.relu)
# 輸出層, 10 個神經元
y_predict=layer(output_dim=10,input_dim=1000, inputs=h2,activation=None)正確率可提升到
Accuracy: 0.9636
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