反向传播中loss函数的可视化代码
时间: 2023-12-11 18:05:47 浏览: 25
以下是使用TensorFlow可视化loss函数的代码示例:
```python
import tensorflow as tf
import matplotlib.pyplot as plt
# 定义模型
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, activation='relu', input_shape=(784,)),
tf.keras.layers.Dense(10, activation='softmax')
])
# 定义损失函数和优化器
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()
# 定义训练函数
@tf.function
def train_step(x, y):
with tf.GradientTape() as tape:
# 前向传播
y_pred = model(x)
# 计算损失函数值
loss = loss_fn(y, y_pred)
# 计算梯度
gradients = tape.gradient(loss, model.trainable_variables)
# 更新模型参数
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return loss
# 定义训练过程
def train(train_dataset, epochs):
losses = []
for epoch in range(epochs):
epoch_loss = 0.0
for step, (x, y) in enumerate(train_dataset):
loss = train_step(x, y)
epoch_loss += loss
losses.append(epoch_loss / (step + 1))
print('Epoch {} loss: {:.4f}'.format(epoch + 1, epoch_loss / (step + 1)))
# 可视化损失函数值
plt.plot(losses)
plt.title('Training Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()
# 训练模型
train(train_dataset, epochs=10)
```
在训练过程中,将每个epoch的损失函数值存储在一个列表中,最后使用matplotlib库将损失函数值可视化出来。