开发人员在tensorflow中使用显式的前向传播和梯度磁带还是Model=Sequential()？

TensorFlow提供了两种方式来构建深度神经网络 - 显式的前向传播和梯度磁带，以及使用Sequential()模型。

使用显式的前向传播和梯度磁带对于构建定制网络或需要更多控制的任务非常有用。以下是使用显式前向传递和梯度磁带的示例：

import tensorflow as tf

# Create the model architecture
inputs = tf.keras.layers.Input(shape=(784,))
dense1 = tf.keras.layers.Dense(units=256, activation='relu')
dense2 = tf.keras.layers.Dense(units=128, activation='relu')
outputs = tf.keras.layers.Dense(units=10, activation='softmax')

# Define the forward pass
x = dense1(inputs)
x = dense2(x)
predictions = outputs(x)

# Compute the loss using cross-entropy
loss = tf.keras.losses.sparse_categorical_crossentropy(labels, predictions)

# Compute the gradients using GradientTape
gradients = tf.gradients(loss, [dense1.weights, dense2.weights, outputs.weights])

# Update the weights using gradients
optimizer.apply_gradients(zip(gradients, [dense1.weights, dense2.weights, outputs.weights]))

使用Sequential()模型更加简单，并且适用于许多常见的深度学习任务：

import tensorflow as tf

# Define the model architecture
model = tf.keras.Sequential([
  tf.keras.layers.Dense(units=256, activation='relu', input_shape=(784,)),
  tf.keras.layers.Dense(units=128, activation='relu'),
  tf.keras.layers.Dense(units=10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam', 
              loss='sparse_categorical_crossentropy', 
              metrics=['accuracy'])

# Train the model
model.fit(train_images, train_labels, epochs=10)

综上所述，开发人员可以选择使用显式的前向传递和梯度磁带或Sequential()模型