ValueError：调用层“sequential_5”时遇到异常（类型Sequential）

Question

我正在学习这门课程： TensorFlow Developer Certificate in 2022: Zero to Mastery

这是以下代码：

# Set random seed
tf.random.set_seed(42)

# Create some regression data
X_regression = np.arange(0, 1000, 5)
y_regression = np.arange(100, 1100, 5)

# Split it into training and test sets
X_reg_train = X_regression[:150]
X_reg_test = X_regression[150:]
y_reg_train = y_regression[:150]
y_reg_test = y_regression[150:]

# Setup random seed
tf.random.set_seed(42)

# Recreate the model
model_3 = tf.keras.Sequential([
  tf.keras.layers.Dense(100),
  tf.keras.layers.Dense(10),
  tf.keras.layers.Dense(1)
])

# Change the loss and metrics of our compiled model
model_3.compile(loss=tf.keras.losses.mae, # change the loss function to be regression-specific
                optimizer=tf.keras.optimizers.Adam(),
                metrics=['mae']) # change the metric to be regression-specific

# Fit the recompiled model
model_3.fit(X_reg_train, y_reg_train, epochs=100)

我收到以下错误：

为什么会出现以下错误，我该如何解决？

Answer 1

您只需为数据添加第二个维度。它必须是 (batch_size, features)。您可以使用np.expand_dims 将您的输入从 (batch_size,) 更改为 (batch_size, features)：

import tensorflow as tf
import numpy as np

tf.random.set_seed(42)

# Create some regression data
X_regression = np.expand_dims(np.arange(0, 1000, 5), axis=1)
y_regression = np.expand_dims(np.arange(100, 1100, 5), axis=1)

# Split it into training and test sets
X_reg_train = X_regression[:150]
X_reg_test = X_regression[150:]
y_reg_train = y_regression[:150]
y_reg_test = y_regression[150:]

tf.random.set_seed(42)

# Recreate the model
model_3 = tf.keras.Sequential([
  tf.keras.layers.Dense(100),
  tf.keras.layers.Dense(10),
  tf.keras.layers.Dense(1)
])

# Change the loss and metrics of our compiled model
model_3.compile(loss=tf.keras.losses.mae, # change the loss function to be regression-specific
                optimizer=tf.keras.optimizers.Adam(),
                metrics=['mae']) # change the metric to be regression-specific

# Fit the recompiled model
model_3.fit(X_reg_train, y_reg_train, epochs=100)