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python - Tensorflow，如何存储变量？

问题描述

所以我刚刚开始对 tensorflow 进行一些试验，但我觉得我很难掌握这个概念，我目前专注于 MNIST 数据集，但其中只有 8000 个用于训练，2000 个用于测试。我目前拥有的小代码片段是：

from keras.layers import Input, Dense, initializers
from keras.models import Model
from Dataset import Dataset
import matplotlib.pyplot as plt
from keras import optimizers, losses
import tensorflow as tf
import keras.backend as K

#global variables
d = Dataset()
num_features = d.X_train.shape[1]
low_dim = 32

def autoencoder():
    w = initializers.RandomNormal(mean=0.0, stddev=0.05, seed=None)
    input = Input(shape=(num_features,))

    encoded = Dense(low_dim, activation='relu', kernel_initializer = w)(input)

    decoded = Dense(num_features, activation='sigmoid', kernel_initializer = w)(encoded)

    autoencoder = Model(input, decoded)
    adam = optimizers.Adagrad(lr=0.01, epsilon=None, decay=0.0)
    autoencoder.compile(optimizer=adam, loss='binary_crossentropy')
    autoencoder.fit(d.X_train, d.X_train,
                    epochs=50,
                    batch_size=64,
                    shuffle=True,
                    )

    encoded_imgs = autoencoder.predict(d.X_test)
    decoded_imgs = autoencoder.predict(encoded_imgs)
    #sess = tf.InteractiveSession()
    #error = losses.mean_absolute_error(decoded_imgs[0], d.X_train[0])
    #print(error.eval())
    #print(decoded_imgs.shape)
    #sess.close()
    n = 20  # how many digits we will display
    plt.figure(figsize=(20, 4))
    for i in range(n):
        # display original
        #sess = tf.InteractiveSession()
        error = losses.mean_absolute_error(decoded_imgs[n], d.X_test[n])
        #print(error.eval())
        #print(decoded_imgs.shape)
        #sess.close()
        ax = plt.subplot(2, n, i + 1)
        plt.imshow(d.X_test[i].reshape(28, 28))
        plt.gray()
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)

        # display reconstruction
        ax = plt.subplot(2, n, i + 1 + n)
        plt.imshow(decoded_imgs[i].reshape(28, 28))
        plt.gray()
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)
    #print(error)
    plt.show()
    return error

我想要做的是将错误存储为一个列表，稍后我可以打印或绘制在图表中，但是如何使用 tensorflow/keras 有效地做到这一点？提前致谢

标签： pythontensorflow

解决方案

您可以使用回调 CSVLogger 将错误存储在 csv 文件中。这是此任务的代码片段。

from keras.callbacks import CSVLogger

# define callbacks
callbacks = [CSVLogger(path_csv_logger, separator=';', append=True)]

# pass callback to model.fit() oder model.fit_generator()
model.fit_generator(
    train_batch, train_steps, epochs=10, callbacks=callbacks,
    validation_data=validation_batch, validation_steps=val_steps)

编辑：为了将错误存储在列表中，您可以使用类似这样的东西

# source https://keras.io/callbacks/
class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = []

    def on_batch_end(self, batch, logs={}):
        self.losses.append(logs.get('loss'))

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