猫狗识别tensorflow代码

以下是使用 TensorFlow 和 Keras API 进行猫狗识别的示例代码：

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 定义训练和验证数据集路径
train_dir = '/path/to/train_data'
validation_dir = '/path/to/validation_data'

# 数据预处理
train_datagen = ImageDataGenerator(rescale=1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        train_dir,
        target_size=(150, 150),
        batch_size=20,
        class_mode='binary')

validation_generator = validation_datagen.flow_from_directory(
        validation_dir,
        target_size=(150, 150),
        batch_size=20,
        class_mode='binary')

# 构建 CNN 模型
model = tf.keras.models.Sequential([
        tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),
        tf.keras.layers.MaxPooling2D(2, 2),
        tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2,2),
        tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2,2),
        tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2,2),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
])

# 编译模型
model.compile(loss='binary_crossentropy',
              optimizer=tf.keras.optimizers.RMSprop(lr=1e-4),
              metrics=['accuracy'])

# 训练模型
history = model.fit_generator(
      train_generator,
      steps_per_epoch=100,
      epochs=20,
      validation_data=validation_generator,
      validation_steps=50,
      verbose=2)

# 使用模型进行预测
import numpy as np
from tensorflow.keras.preprocessing import image

img_path = '/path/to/test_image.jpg'
img = image.load_img(img_path, target_size=(150, 150))
img_tensor = image.img_to_array(img)
img_tensor = np.expand_dims(img_tensor, axis=0)
img_tensor /= 255.

prediction = model.predict(img_tensor)
if prediction < 0.5:
    print('猫')
else:
    print('狗')

需要注意的是，上述代码中的 `train_dir` 和 `validation_dir` 需要替换成对应的训练和验证数据集的路径。