Tensorflow，如何存儲變量？

Question

所以我剛剛開始對 tensorflow 進行一些實驗，但我覺得我很難掌握這個概念，我目前專注於 MNIST 數據集，但只有 8000 個用作訓練，2000 個用於測試。 我目前擁有的小代碼片段是：

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

我想要做的是將錯誤存儲為一個列表，稍后我可以將其打印或繪制在圖表中，但是如何使用 tensorflow/keras 有效地做到這一點？ 提前致謝

Answer 1

您可以使用回調 CSVLogger 將錯誤存儲在 csv 文件中。 這是此任務的代碼片段。

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)

編輯：為了在列表中存儲錯誤，你可以使用這樣的東西

# 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'))