这个损失函数的计算公式为: ``` Categorical Crossentropy = -1/batch_size * (sum(Y*log(p)) + (1-Y)*log(1-p)) ``` 其中,`Y`是目标值(one-hot编码),`p`是预测值(模型输出)。注意,这里的`p`是通过softmax函数转换过的,所以它的值域在0到1之间。
SparseCategoricalCrossentropy( from_logits=True, reduction='none', ) real = tf.constant([[2,3,4], [1,2,3]], dtype=tf.float32) pred = tf.constant([ [[1.0, 2.0, 3.0, 4.0, 5.0], [2.0, 3.0, 4.0, 5.0, 6.0], [3.0, 4.0, 5.0, 6.0, 7.0]], [[1.0, 2.0, 3.0, 4.0, 5.0],...
print("Training...Please wait") model.compile(optimizer='adam', loss="categorical_crossentropy", metrics=["accuracy"]) model.fit_generator(gen.flow(X_train, y_train, batch_size=batch_size), epochs=epochs, validation_data=(X_val, y_val), verbose=2, steps_per_epoch=X_train.shape[0] ...
2.3.编译设置 损失函数(loss):用于衡量模型在训练期间的准确率,这里用sparse_categorical_crossentropy,原理与categorical_crossentropy(多类交叉熵损失 )一样,不过真实值采用的整数编码(例如第0个类用数字0表示,第3个类用数字3表示,官方可看:tf.keras.losses.SparseCategoricalCrossentropy) 优化器(optimizer):决定模型...
loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(x_...
probs = policy_network(state) # Note that this is equivalent to what used to be called multinomial m = Categorical(probs) action = m.sample() next_state, reward = env.step(action) loss = -m.log_prob(action) * reward loss.backward() 对照一下,这个-m.log_prob(action)应该对应上述公...
pytorch混合分布的工作方式是通过在原始的Normal分布上使用3个额外的分布Independent、Categorical和MixtureSameFamily来实现的。从本质上讲,它创建了一个混合,基于给定Categorical分布的概率权重。因为我们的新均值和标准集有一个额外的轴,这个轴被用作独立的轴,需要决定从中得出哪个均值/标准集的值。
为了与 PyTorch 中torch.nn.CrossEntropyLoss()求交叉熵的方法一致,Tensorflow 中并未对label 进行 One-Hot 编码,所以使用了tf.losses.sparse_categorical_crossentropy()方法计算交叉熵。结果为: Model:"cnn_model_2"___ Layer(type)Output Shape Param#===sequential_2(Sequential)multiple3148===...
CrossEntropyLoss（或NLLLoss）要求target类型为Long。例如，下面的代码引发RuntimeError：...
loss <- nnf_cross_entropy(output, b[[2]]$to(device = "cuda")) Unlike categorical cross entropy inkeras, which would expectpredictionto contain probabilities, as obtained by applying asoftmaxactivation,torch’snnf_cross_entropy()works with the raw outputs (thelogits). This is why the networ...