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],...
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_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) # metric train_loss_metric = tf.keras.metrics.Mean(name='train_loss') train_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy') test_loss_metric = tf.keras.metrics.Mean(name='test_loss')...
loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(x_...
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===...
model.compile(loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), optimizer=keras.optimizers.Nadam(learning_rate=learning_rate), metrics=[keras.metrics.sparse_categorical_accuracy]) callbacks = [] return model, callbacks class Classifier(keras.Model): ...
(3) 优化器:Torch 提供了多种优化器,如 SparseCategoricalCrossEntropyLoss、MSELoss 等,它们可以针对不同类型的问题自动选择合适的优化策略,进一步提高训练效率。 4.总结 随着深度学习模型的复杂度不断提高,Torch 模型的参数量和计算量也呈现出快速增长的趋势。为了应对这一挑战,Torch 提供了多种优化技术,如随机梯度...
( loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"] ) # Throws: TypeError: compile() got an unexpected keyword argument 'loss' model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) # Throws: TypeError: compile() got an...
cross_entropy¶ torch.nn.functional.cross_entropy(input, target, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean')[source]¶ This criterion combines log_softmax and nll_loss in a single function. See CrossEntropyLoss for details. Parameters input (Tensor)–...
sparse.R' 'nn-upsampling.R' 'nn-utils-clip-grad.R' 'nn-utils-rnn.R' 'nn-utils-weight-norm.R' 'nn-utils.R' 'nn_adaptive.R' 'nnf-activation.R' 'nnf-batchnorm.R' 'nnf-conv.R' 'nnf-distance.R' 'nnf-dropout.R' 'nnf-embedding.R' 'nnf-fold.R' 'nnf-instancenorm.R' 'nnf-...