Tensorflow二值图像分类的概率

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten
from tensorflow.keras.optimizers import Adam

# 加载数据集（这里以CIFAR-10为例，实际上你需要自己的二值图像数据集）
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()

# 数据预处理
x_train, x_test = x_train / 255.0, x_test / 255.0

# 构建模型
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(1, activation='sigmoid')  # 使用sigmoid激活函数，适用于二分类问题
])

# 编译模型，设置output_mode为'probability'
model.compile(optimizer=Adam(learning_rate=0.001),
              loss='binary_crossentropy',
              metrics=['accuracy'],
              output_mode='probability')

# 训练模型
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# 使用模型进行预测并获取概率值
predictions = model.predict(x_test)