optimizer = keras.optimizers.Adam()

这段代码定义了一个 Adam 优化器，并将其赋值给 optimizer 变量。Adam 优化器是一种基于梯度的优化算法，它可以自适应地调整学习率，同时具有一定的正则化效果，可以有效地加速深度神经网络的训练过程。在 Keras 中，可以通过调用 keras.optimizers.Adam() 函数来创建一个 Adam 优化器对象。

相关问题

model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.SparseCategoricalCrossentropy(), metrics=['accuracy'])

This code compiles a machine learning model using the Adam optimizer, the Sparse Categorical Crossentropy loss function, and accuracy as the evaluation metric.

The Adam optimizer is a popular optimization algorithm that uses adaptive learning rates to converge faster and more efficiently than other optimization algorithms.

The Sparse Categorical Crossentropy loss function is commonly used for multi-class classification problems where the target variable is represented as integers. It computes the cross-entropy loss between the predicted and true labels, and penalizes the model for incorrect predictions.

The accuracy metric measures how often the model makes correct predictions. It is a common evaluation metric for classification problems.

def build_model(self, input_shape, nb_classes): x = keras.layers.Input(input_shape) conv1 = keras.layers.Conv1D(128, 8, 1, padding='same')(x) conv1 = keras.layers.BatchNormalization()(conv1) conv1 = keras.layers.Activation('relu')(conv1) conv2 = keras.layers.Conv1D(256, 5, 1, padding='same')(conv1) conv2 = keras.layers.BatchNormalization()(conv2) conv2 = keras.layers.Activation('relu')(conv2) conv3 = keras.layers.Conv1D(128, 3, 1, padding='same')(conv2) conv3 = keras.layers.BatchNormalization()(conv3) conv3 = keras.layers.Activation('relu')(conv3) full = keras.layers.GlobalAveragePooling1D()(conv3) out = keras.layers.Dense(nb_classes, activation='softmax')(full) model = keras.models.Model(inputs=x, outputs=out) # optimizer = keras.optimizers.Adam() optimizer = keras.optimizers.Nadam() model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) return model

这是在 Keras 框架中定义一个模型的代码。这个模型由输入层、三个卷积层、全局平均池化层、全连接层和输出层组成。卷积层之间还有批归一化层和激活层。这个模型的输入尺寸为 `input_shape` ，输出的类别数为 `nb_classes` 。