49 lines
2.2 KiB
Python
49 lines
2.2 KiB
Python
import tensorflow as tf
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import numpy as np
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import os
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#这个脚本回到drive的利用率特别高，超出100%
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"""
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def load_mnist(path):
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#加载本地下载好的mnist数据集
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f = np.load(path)
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x_train, y_train = f['x_train'], f['y_train']
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x_test, y_test = f['x_test'], f['y_test']
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f.close()
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return (x_train, y_train), (x_test, y_test)
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(x_train, y_train), (x_test, y_test) = load_mnist("mnist.npz")
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"""
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mnist = tf.keras.datasets.mnist#从xx网站下载mnist到.kera,如果已经有了直接使用
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train, x_test = x_train / 255.0, x_test / 255.0 # 将样本从整数转换为浮点数
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# 利用tf.keras.Sequential容器封装网络层，前一层网络的输出默认作为下一层的输入
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model = tf.keras.models.Sequential([
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tf.keras.layers.Flatten(input_shape=(28, 28)),
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tf.keras.layers.Dense(128, activation='relu'), # 创建一层网络，设置输出节点数为128，激活函数类型为Relu
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tf.keras.layers.Dropout(0.2), # 在训练中每次更新时， 将输入单元的按比率随机设置为 0， 这有助于防止过拟合
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tf.keras.layers.Dense(10, activation='softmax')]) # Dense层就是所谓的全连接神经网络层
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model.summary()#显示模型的结构
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# 为训练选择优化器和损失函数：
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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if 'CLOUD_PROVIDER' in os.environ and os.environ['CLOUD_PROVIDER'] == 'Agit':
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log_dir = os.path.join('/root/.agit/logs') # this is the storage path in the Agit environment
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else:
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log_dir = os.path.join("logs") # this is the path when the program runs in other environments
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#log_dir = os.path.join("logs")
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# print(log_dir)
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if not os.path.exists(log_dir):
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os.mkdir(log_dir)
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# 定义TensorBoard对象.histogram_freq 如果设置为0，则不会计算直方图。
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tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
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# TensorBoard对象作为回调传给model.fit方法
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model.fit(x_train, y_train, epochs=8, validation_data=(x_test, y_test), callbacks=[tensorboard_callback])
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model.save_weights(log_dir + '/weight/my_weights', save_format='tf') # 保存模型*****直接引用对应的路径参数 |