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61 lines
1.7 KiB
61 lines
1.7 KiB
import os
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import tensorflow as tf
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from tensorflow import keras
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import matplotlib.pyplot as plt
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print(tf.version.VERSION)
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(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
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plt.imshow(train_images[0])
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train_labels = train_labels[:1000]
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test_labels = test_labels[:1000]
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train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0
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test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0
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# 定义一个简单的序列模型
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def create_model():
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model = tf.keras.models.Sequential([
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keras.layers.Dense(512, activation='relu', input_shape=(784,)),
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keras.layers.Dropout(0.2),
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keras.layers.Dense(10)
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])
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model.compile(optimizer='adam',
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loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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return model
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# 创建一个基本的模型实例
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model = create_model()
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# 显示模型的结构
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model.summary()
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checkpoint_path = "training_1/cp.ckpt"
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checkpoint_dir = os.path.dirname(checkpoint_path)
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# 创建一个保存模型权重的回调
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cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
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save_weights_only=True,
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verbose=1)
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# 使用新的回调训练模型
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model.fit(train_images,
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train_labels,
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epochs=10,
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batch_size=8,
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validation_data=(test_images,test_labels),
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callbacks=[cp_callback]) # 通过回调训练
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# 这可能会生成与保存优化程序状态相关的警告。
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# 这些警告（以及整个笔记本中的类似警告）
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# 是防止过时使用，可以忽略。
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results = model.evaluate(test_images, test_labels, verbose=2)
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print(results) |