机器学习|【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])

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