浅谈keras中loss与val_loss的关系_Python

浅谈keras中loss与val_loss的关系

2020-06-23 10:12lgy_keira Python

这篇文章主要介绍了浅谈keras中loss与val_loss的关系，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

loss函数如何接受输入值

keras封装的比较厉害，官网给的例子写的云里雾里，

在stackoverflow找到了答案

You can wrap the loss function as a inner function and pass your input tensor to it (as commonly done when passing additional arguments to the loss function).

									def custom_loss_wrapper(input_tensor):

									 def custom_loss(y_true, y_pred):

									  return K.binary_crossentropy(y_true, y_pred) + K.mean(input_tensor)

									 return custom_loss

									input_tensor = Input(shape=(10,))

									hidden = Dense(100, activation='relu')(input_tensor)

									out = Dense(1, activation='sigmoid')(hidden)

									model = Model(input_tensor, out)

									model.compile(loss=custom_loss_wrapper(input_tensor), optimizer='adam')

You can verify that input_tensor and the loss value will change as different X is passed to the model.

									X = np.random.rand(1000, 10)

									y = np.random.randint(2, size=1000)

									model.test_on_batch(X, y) # => 1.1974642

									X *= 1000

									model.test_on_batch(X, y) # => 511.15466

fit_generator

fit_generator ultimately calls train_on_batch which allows for x to be a dictionary.

Also, it could be a list, in which casex is expected to map 1:1 to the inputs defined in Model(input=[in1, …], …)

									### generator

									yield [inputX_1,inputX_2],y

									### model

									model = Model(inputs=[inputX_1,inputX_2],outputs=...)

补充知识：学习keras时对loss函数不同的选择，则model.fit里的outputs可以是one_hot向量，也可以是整形标签

我就废话不多说了，大家还是直接看代码吧~

									from __future__ import absolute_import, division, print_function, unicode_literals

									import tensorflow as tf

									from tensorflow import keras

									import numpy as np

									import matplotlib.pyplot as plt

									print(tf.__version__)

									fashion_mnist = keras.datasets.fashion_mnist

									(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

									class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',

									    'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

									# plt.figure()

									# plt.imshow(train_images[0])

									# plt.colorbar()

									# plt.grid(False)

									# plt.show()

									train_images = train_images / 255.0

									test_images = test_images / 255.0

									# plt.figure(figsize=(10,10))

									# for i in range(25):

									#  plt.subplot(5,5,i+1)

									#  plt.xticks([])

									#  plt.yticks([])

									#  plt.grid(False)

									#  plt.imshow(train_images[i], cmap=plt.cm.binary)

									#  plt.xlabel(class_names[train_labels[i]])

									# plt.show()

									model = keras.Sequential([

									 keras.layers.Flatten(input_shape=(28, 28)),

									 keras.layers.Dense(128, activation='relu'),

									 keras.layers.Dense(10, activation='softmax')

									])

									model.compile(optimizer='adam',

									    loss='categorical_crossentropy', 

									    #loss = 'sparse_categorical_crossentropy' 则之后的label不需要变成one_hot向量，直接使用整形标签即可

									    metrics=['accuracy'])

									one_hot_train_labels = keras.utils.to_categorical(train_labels, num_classes=10)

									model.fit(train_images, one_hot_train_labels, epochs=10)

									one_hot_test_labels = keras.utils.to_categorical(test_labels, num_classes=10)

									test_loss, test_acc = model.evaluate(test_images, one_hot_test_labels)

									print('\nTest accuracy:', test_acc)

									# predictions = model.predict(test_images)

									# predictions[0]

									# np.argmax(predictions[0])

									# test_labels[0]