【Python】逻辑回归（softmax多分类）

Tensorflow实现逻辑回归-softmax多分类

import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# softmax多分类
(train_image, train_label), (test_image, test_label) = tf.keras.datasets.fashion_mnist.load_data()

print(train_image.shape)
print(train_label.shape)
print(test_image.shape)
print(test_label.shape)

plt.imshow(train_image[0])
plt.show()

plt.imshow(test_image[0])
plt.show()

model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])
model.fit(train_image, train_label, epochs=5)

# 测试集
model.evaluate(test_image, test_label)

# 独热编码 onehot
train_label_onehot = tf.keras.utils.to_categorical(train_label)
print(train_label_onehot)

test_label_onehot = tf.keras.utils.to_categorical(test_label)
print(test_label_onehot)

model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
model.fit(train_image, train_label_onehot, epochs=5)

predict = model.predict(test_image)
print(predict.shape)
print(predict[0])
print(np.argmax(predict[0]))
print(test_label[0])