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) # => 1.1974642X *= 1000model.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, …], …)

### generatoryield [inputX_1,inputX_2],y### modelmodel = 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_literalsimport tensorflow as tffrom tensorflow import kerasimport numpy as npimport matplotlib.pyplot as pltprint(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 / 255.0test_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

感谢你能够认真阅读完这篇文章，希望小编分享keras中loss与val_loss的关系是什么？内容对大家有帮助，同时也希望大家多多支持亿速云，关注亿速云行业资讯频道，遇到问题就找亿速云，详细的解决方法等着你来学习!