import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
(train_image,train_label),(test_image,test_label) = tf.keras.datasets.fashion_mnist.load_data()
# 加载keras里的fashion_mnist数据
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
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train_image.shape
# 60000张28*28的图片
(60000, 28, 28)
train_label.shape
(60000,)
test_image.shape,test_label.shape
((10000, 28, 28), (10000,))
plt.imshow(train_image[0])
# imshow()接收一张图像，只是画出该图，并不会立刻显示出来。
# imshow后还可以进行其他draw操作，比如scatter散点等。
# 所有画完后使用plt.show()才能进行结果的显示。如果前面加了%matplotlib inline就不需要plt.show()可以直接显示
<matplotlib.image.AxesImage at 0x23fc68281f0>
train_image[0] # rgb： 0-255
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 13, 73, 0, 0, 1, 4, 0, 0, 0, 0, 1,
1, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
0, 36, 136, 127, 62, 54, 0, 0, 0, 1, 3, 4, 0,
0, 3],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6,
0, 102, 204, 176, 134, 144, 123, 23, 0, 0, 0, 0, 12,
10, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 155, 236, 207, 178, 107, 156, 161, 109, 64, 23, 77, 130,
72, 15],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
69, 207, 223, 218, 216, 216, 163, 127, 121, 122, 146, 141, 88,
172, 66],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,
200, 232, 232, 233, 229, 223, 223, 215, 213, 164, 127, 123, 196,
229, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
183, 225, 216, 223, 228, 235, 227, 224, 222, 224, 221, 223, 245,
173, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
193, 228, 218, 213, 198, 180, 212, 210, 211, 213, 223, 220, 243,
202, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 12,
219, 220, 212, 218, 192, 169, 227, 208, 218, 224, 212, 226, 197,
209, 52],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 99,
244, 222, 220, 218, 203, 198, 221, 215, 213, 222, 220, 245, 119,
167, 56],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 55,
236, 228, 230, 228, 240, 232, 213, 218, 223, 234, 217, 217, 209,
92, 0],
[ 0, 0, 1, 4, 6, 7, 2, 0, 0, 0, 0, 0, 237,
226, 217, 223, 222, 219, 222, 221, 216, 223, 229, 215, 218, 255,
77, 0],
[ 0, 3, 0, 0, 0, 0, 0, 0, 0, 62, 145, 204, 228,
207, 213, 221, 218, 208, 211, 218, 224, 223, 219, 215, 224, 244,
159, 0],
[ 0, 0, 0, 0, 18, 44, 82, 107, 189, 228, 220, 222, 217,
226, 200, 205, 211, 230, 224, 234, 176, 188, 250, 248, 233, 238,
215, 0],
[ 0, 57, 187, 208, 224, 221, 224, 208, 204, 214, 208, 209, 200,
159, 245, 193, 206, 223, 255, 255, 221, 234, 221, 211, 220, 232,
246, 0],
[ 3, 202, 228, 224, 221, 211, 211, 214, 205, 205, 205, 220, 240,
80, 150, 255, 229, 221, 188, 154, 191, 210, 204, 209, 222, 228,
225, 0],
[ 98, 233, 198, 210, 222, 229, 229, 234, 249, 220, 194, 215, 217,
241, 65, 73, 106, 117, 168, 219, 221, 215, 217, 223, 223, 224,
229, 29],
[ 75, 204, 212, 204, 193, 205, 211, 225, 216, 185, 197, 206, 198,
213, 240, 195, 227, 245, 239, 223, 218, 212, 209, 222, 220, 221,
230, 67],
[ 48, 203, 183, 194, 213, 197, 185, 190, 194, 192, 202, 214, 219,
221, 220, 236, 225, 216, 199, 206, 186, 181, 177, 172, 181, 205,
206, 115],
[ 0, 122, 219, 193, 179, 171, 183, 196, 204, 210, 213, 207, 211,
210, 200, 196, 194, 191, 195, 191, 198, 192, 176, 156, 167, 177,
210, 92],
[ 0, 0, 74, 189, 212, 191, 175, 172, 175, 181, 185, 188, 189,
188, 193, 198, 204, 209, 210, 210, 211, 188, 188, 194, 192, 216,
170, 0],
[ 2, 0, 0, 0, 66, 200, 222, 237, 239, 242, 246, 243, 244,
221, 220, 193, 191, 179, 182, 182, 181, 176, 166, 168, 99, 58,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 40, 61, 44, 72, 41, 35,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0]], dtype=uint8)
train_label
# 第一个图像的类别是9，第二个是0，...倒数第一个是5
array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)
train_image = train_image/255 # 对训练集进行归一化
test_image = test_image/255
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28))) # 参考https://blog.csdn.net/qq_46244851/article/details/109584831
model.add(tf.keras.layers.Dense(128,activation='relu'))
model.add(tf.keras.layers.Dense(10,activation='softmax')) # 输出十个类，用softmax转化成概率值激活
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy', # 当label使用数字编码时就使用这个损失函数
metrics=['acc']
)
model.fit(train_image,train_label,epochs=5)
Epoch 1/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.5045 - acc: 0.8214
Epoch 2/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3787 - acc: 0.8632
Epoch 3/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3376 - acc: 0.8781
Epoch 4/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3125 - acc: 0.8847
Epoch 5/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.2980 - acc: 0.8899
<keras.callbacks.History at 0x23fc62a45e0>
model.evaluate(test_image,test_label)
313/313 [==============================] - 0s 779us/step - loss: 0.3516 - acc: 0.8758
[0.35156339406967163, 0.8758000135421753]
train_label
array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)
独热编码，比如：
上海用[1,0,0]表示
北京用[0,1,0]表示
深圳用[0,0,1]表示
独热编码即 One-Hot 编码，又称一位有效编码，其方法是使用N位状态寄存器来对N个状态进行编码，每个状态都有它独立的寄存器位，并且在任意时候，其中只有一位有效。
train_label_onehot = tf.keras.utils.to_categorical(train_label)# 将train_label转换成独热编码
train_label_onehot
array([[0., 0., 0., ..., 0., 0., 1.],
[1., 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., 0., ..., 0., 0., 0.]], dtype=float32)
test_label_onehot = tf.keras.utils.to_categorical(test_label)
test_label_onehot
array([[0., 0., 0., ..., 0., 0., 1.],
[0., 0., 1., ..., 0., 0., 0.],
[0., 1., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 1., 0.],
[0., 1., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]], dtype=float32)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28))) # 参考https://blog.csdn.net/qq_46244851/article/details/109584831
model.add(tf.keras.layers.Dense(128,activation='relu'))
model.add(tf.keras.layers.Dense(10,activation='softmax')) # 输出十个类，用softmax转化成概率值激活
model.compile(
optimizer='adam',
loss='categorical_crossentropy', # 独热编码时，就用categorical_crossentropy
metrics=['acc']
)
model.fit(train_image,train_label_onehot,epochs=5)
Epoch 1/5
1875/1875 [==============================] - 2s 1ms/step - loss: 0.4965 - acc: 0.8247
Epoch 2/5
1875/1875 [==============================] - 2s 1ms/step - loss: 0.3729 - acc: 0.8636
Epoch 3/5
1875/1875 [==============================] - 2s 1ms/step - loss: 0.3368 - acc: 0.8774
Epoch 4/5
1875/1875 [==============================] - 2s 1ms/step - loss: 0.3124 - acc: 0.8853
Epoch 5/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.2946 - acc: 0.8922
<keras.callbacks.History at 0x23fca64b850>
predict=model.predict(test_image)
predict.shape
(10000, 10)
predict[0]
array([1.4467735e-05, 6.7211374e-08, 5.7228771e-08, 1.8838726e-06,
1.9777593e-07, 1.3431358e-02, 5.4569400e-06, 6.4479321e-02,
6.1685896e-05, 9.2200553e-01], dtype=float32)
np.argmax(predict[0]) # 第一个预测的参数
9
test_label[0]
9
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28))) # 参考https://blog.csdn.net/qq_46244851/article/details/109584831
model.add(tf.keras.layers.Dense(128,activation='relu'))
model.add(tf.keras.layers.Dense(10,activation='softmax')) # 输出十个类，用softmax转化成概率值激活
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), # 定义学习率为0.01，摁下shift+tab可以查看函数的用法，摁下tab+tab可以提示
loss='categorical_crossentropy', # 独热编码时，就用categorical_crossentropy
metrics=['acc']
)
model.fit(train_image,train_label_onehot,epochs=5)
Epoch 1/5
1875/1875 [==============================] - 2s 1ms/step - loss: 0.3720 - acc: 0.8651
Epoch 2/5
1875/1875 [==============================] - 2s 1ms/step - loss: 0.3728 - acc: 0.8668
Epoch 3/5
1875/1875 [==============================] - 2s 1ms/step - loss: 0.3642 - acc: 0.8694
Epoch 4/5
1875/1875 [==============================] - 2s 1ms/step - loss: 0.3641 - acc: 0.8682
Epoch 5/5
1875/1875 [==============================] - 2s 1ms/step - loss: 0.3713 - acc: 0.8676
<keras.callbacks.History at 0x23fcba05fa0>