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
以上这篇浅谈keras中loss与val_loss的关系就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。
P70系列延期,华为新旗舰将在下月发布
3月20日消息,近期博主@数码闲聊站 透露,原定三月份发布的华为新旗舰P70系列延期发布,预计4月份上市。
而博主@定焦数码 爆料,华为的P70系列在定位上已经超过了Mate60,成为了重要的旗舰系列之一。它肩负着重返影像领域顶尖的使命。那么这次P70会带来哪些令人惊艳的创新呢?
根据目前爆料的消息来看,华为P70系列将推出三个版本,其中P70和P70 Pro采用了三角形的摄像头模组设计,而P70 Art则采用了与上一代P60 Art相似的不规则形状设计。这样的外观是否好看见仁见智,但辨识度绝对拉满。
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