深度学习代码进行判断两张图片的相似性

以下是基于Siamese网络的深度学习代码，用于判断两张图片的相似性：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, Flatten, Dense, Lambda
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
from tensorflow.keras.optimizers import Adam
import numpy as np

def euclidean_distance(vectors):
    v1, v2 = vectors
    sum_square = K.sum(K.square(v1 - v2), axis=1, keepdims=True)
    return K.sqrt(K.maximum(sum_square, K.epsilon()))

def create_base_network(input_shape):
    input = Input(shape=input_shape)
    x = Conv2D(32, (3, 3), activation='relu')(input)
    x = Conv2D(64, (3, 3), activation='relu')(x)
    x = Conv2D(128, (3, 3), activation='relu')(x)
    x = Flatten()(x)
    x = Dense(512, activation='relu')(x)
    x = Dense(128, activation='relu')(x)
    model = Model(input, x)
    return model

def create_siamese_network(input_shape):
    input_a = Input(shape=input_shape)
    input_b = Input(shape=input_shape)

    base_network = create_base_network(input_shape)
    processed_a = base_network(input_a)
    processed_b = base_network(input_b)

    distance = Lambda(euclidean_distance)([processed_a, processed_b])

    model = Model([input_a, input_b], distance)
    return model

# 构建训练数据
def create_pairs(x, digit_indices):
    pairs = []
    labels = []
    n = min([len(digit_indices[d]) for d in range(10)]) - 1
    for d in range(10):
        for i in range(n):
            z1, z2 = digit_indices[d][i], digit_indices[d][i + 1]
            pairs += [[x[z1], x[z2]]]
            inc = np.random.randint(1, 10)
            dn = (d + inc) % 10
            z1, z2 = digit_indices[d][i], digit_indices[dn][i]
            pairs += [[x[z1], x[z2]]]
            labels += [1, 0]
    return np.array(pairs), np.array(labels)

# 训练模型
def train_model():
    input_shape = (28, 28, 1)
    model = create_siamese_network(input_shape)
    optimizer = Adam(lr=0.00006)
    model.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=['accuracy'])

    # 构建训练数据
    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
    x_train = x_train.astype('float32') / 255.
    x_test = x_test.astype('float32') / 255.
    x_train = np.expand_dims(x_train, axis=-1)
    x_test = np.expand_dims(x_test, axis=-1)
    digit_indices = [np.where(y_train == i)[0] for i in range(10)]
    train_pairs, train_y = create_pairs(x_train, digit_indices)

    # 训练模型
    model.fit([train_pairs[:, 0], train_pairs[:, 1]], train_y,
              batch_size=128,
              epochs=20,
              validation_split=0.2)

    return model

# 测试模型
def test_model(model):
    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
    x_train = x_train.astype('float32') / 255.
    x_test = x_test.astype('float32') / 255.
    x_train = np.expand_dims(x_train, axis=-1)
    x_test = np.expand_dims(x_test, axis=-1)
    digit_indices = [np.where(y_train == i)[0] for i in range(10)]
    test_pairs, test_y = create_pairs(x_test, digit_indices)
    loss, acc = model.evaluate([test_pairs[:, 0], test_pairs[:, 1]], test_y)
    print("Test accuracy: {:.2f}%".format(acc * 100))

model = train_model()
test_model(model)

这段代码使用了MNIST数据集，构建了一个Siamese网络，训练数据包含相似图片和不相似图片，通过最小化相似图片之间的距离和不相似图片之间的距离，来训练模型。测试数据同样是相似图片和不相似图片，用来测试模型的准确率。