51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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from tensorflow.keras.preprocessing import image
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from tensorflow.keras.models import load_model
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import tensorflow as tf
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# # 加载Fashion-MNIST数据集
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# fashion_mnist = tf.keras.datasets.fashion_mnist
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# (X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()
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#
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# # 数据预处理
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# X_train = X_train / 255.0 # 将像素值缩放到0-1之间
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# X_test = X_test / 255.0
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#
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# # 加载模型
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# model = load_model('fashion_mnist_model.h5')
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#
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# # 评估模型
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# loss, accuracy = model.evaluate(X_test, y_test)
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# print(f'Test Loss: {loss}')
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# print(f'Test Accuracy: {accuracy}')
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# 图像文件路径
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img_path = '运动鞋.png'
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# 加载图像并调整大小
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img = image.load_img(img_path, target_size=(28, 28), color_mode='grayscale')
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# 将PIL图像转换为NumPy数组
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img_array = image.img_to_array(img) / 255.0
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# 添加批量维度
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img_array = np.expand_dims(img_array, axis=0)
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# 显示图像
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plt.imshow(img_array[0, :, :, 0], cmap='gray')
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plt.show()
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# 打印图像数组形状
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print(f'Image array shape: {img_array.shape}')
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# 加载训练好的模型
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model = load_model('fashion_mnist_model.h5')
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# 进行预测
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predictions = model.predict(img_array)
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predicted_class = np.argmax(predictions, axis=1)
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print(f'Predicted class: {predicted_class[0]}')
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