不懂keras中loss与val_loss的关系是什么？？其实想解决这个问题也不难，下面让小编带着大家一起学习怎么去解决，希望大家阅读完这篇文章后大所收获。
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]loss若为loss=‘categorical_crossentropy', 则fit中的第二个输出必须是一个one_hot类型，
而若loss为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
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