import warnings
warnings.simplefilter(action='ignore')
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train_flatten = x_train.reshape(x_train.shape[0], 784).astype('float32')
x_test_flatten = x_test.reshape(x_test.shape[0], 784).astype('float32')
x_train_normalize = x_train_flatten / 255.0
x_test_normalize = x_test_flatten / 255.0
y_train_one_hot = tf.keras.utils.to_categorical(y_train)
y_test_one_hot = tf.keras.utils.to_categorical(y_test)
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(units=1000, input_dim=784, kernel_initializer='normal', activation='relu'), # 输入层-隐藏层(这里隐藏层为1000个神经元)
tf.keras.layers.Dropout(0.5), # 添加 Dropout 层
tf.keras.layers.Dense(units=10, kernel_initializer='normal', activation='softmax') # 输出层
])
print(model.summary())
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 1000) 785000 _________________________________________________________________ dropout (Dropout) (None, 1000) 0 _________________________________________________________________ dense_1 (Dense) (None, 10) 10010 ================================================================= Total params: 795,010 Trainable params: 795,010 Non-trainable params: 0 _________________________________________________________________ None
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
train_history = model.fit(x=x_train_normalize, y=y_train_one_hot, validation_split=0.2,
epochs=10, batch_size=200, verbose=2)
Train on 48000 samples, validate on 12000 samples Epoch 1/10 - 6s - loss: 12.5889 - acc: 0.2132 - val_loss: 8.5766 - val_acc: 0.4606 Epoch 2/10 - 6s - loss: 8.9286 - acc: 0.4399 - val_loss: 6.4463 - val_acc: 0.5967 Epoch 3/10 - 6s - loss: 7.3299 - acc: 0.5411 - val_loss: 5.2655 - val_acc: 0.6690 Epoch 4/10 - 6s - loss: 6.1830 - acc: 0.6118 - val_loss: 4.8476 - val_acc: 0.6966 Epoch 5/10 - 6s - loss: 5.5846 - acc: 0.6496 - val_loss: 4.5499 - val_acc: 0.7151 Epoch 6/10 - 7s - loss: 5.2927 - acc: 0.6680 - val_loss: 4.4172 - val_acc: 0.7237 Epoch 7/10 - 6s - loss: 4.6555 - acc: 0.7070 - val_loss: 3.3079 - val_acc: 0.7922 Epoch 8/10 - 6s - loss: 4.2692 - acc: 0.7315 - val_loss: 3.1796 - val_acc: 0.7995 Epoch 9/10 - 6s - loss: 4.0309 - acc: 0.7461 - val_loss: 3.1166 - val_acc: 0.8041 Epoch 10/10 - 6s - loss: 3.7557 - acc: 0.7634 - val_loss: 2.8592 - val_acc: 0.8195
def show_train_history(train_history, train, validation):
plt.plot(train_history.history[train])
plt.plot(train_history.history[validation])
plt.title('Train History')
plt.xlabel('Epoch')
plt.ylabel(train)
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
show_train_history(train_history, 'acc', 'val_acc')
show_train_history(train_history, 'loss', 'val_loss')
scores = model.evaluate(x_test_normalize, y_test_one_hot)
print()
print('accuracy:', scores[1])
10000/10000 [==============================] - 1s 91us/step accuracy: 0.8217
predictions = model.predict_classes(x_test_normalize)
predictions
array([9, 2, 1, ..., 4, 5, 6])
def plot_images_labels_prediction(images, labels, predictions, idx, num=10):
"""
images: 数字图像数组
labels: 真实值数组
predictions: 预测结果数据
idx: 开始显示的数据index
num: 要显示的数据项数, 默认为10, 不超过25
"""
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, i+1)
ax.imshow(images[idx], cmap='binary')
title = 'lable=' + str(labels[idx])
if len(predictions) > 0:
title += ',predict=' + str(predictions[idx])
ax.set_title(title, fontsize=10)
ax.set_xticks([])
ax.set_yticks([])
idx += 1
plt.show()
plot_images_labels_prediction(x_test, y_test, predictions, idx=0, num=10)
pd.crosstab(y_test, predictions, rownames=['label'], colnames=['predict'])
predict | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|---|
label | ||||||||||
0 | 956 | 0 | 1 | 1 | 0 | 2 | 14 | 0 | 5 | 1 |
1 | 0 | 1103 | 14 | 1 | 1 | 2 | 6 | 0 | 7 | 1 |
2 | 19 | 3 | 908 | 18 | 9 | 2 | 30 | 3 | 30 | 10 |
3 | 5 | 3 | 18 | 895 | 1 | 51 | 6 | 0 | 14 | 17 |
4 | 1 | 0 | 4 | 3 | 911 | 0 | 25 | 1 | 5 | 32 |
5 | 10 | 4 | 7 | 27 | 8 | 798 | 24 | 0 | 10 | 4 |
6 | 13 | 3 | 2 | 1 | 8 | 17 | 914 | 0 | 0 | 0 |
7 | 31 | 29 | 57 | 36 | 43 | 2 | 6 | 79 | 13 | 732 |
8 | 19 | 14 | 27 | 42 | 19 | 45 | 31 | 4 | 753 | 20 |
9 | 19 | 9 | 6 | 11 | 43 | 8 | 3 | 3 | 7 | 900 |
df = pd.DataFrame({'label': y_test, 'predict': predictions})
df[:2]
label | predict | |
---|---|---|
0 | 7 | 9 |
1 | 2 | 2 |
df[(df.label==5)&(df.predict==2)]
label | predict | |
---|---|---|
1032 | 5 | 2 |
6324 | 5 | 2 |
6385 | 5 | 2 |
6392 | 5 | 2 |
6706 | 5 | 2 |
7542 | 5 | 2 |
7797 | 5 | 2 |
plot_images_labels_prediction(x_test, y_test, predictions, idx=340, num=1)
plot_images_labels_prediction(x_test, y_test, predictions, idx=1289, num=1)