pythontensorflowmachine-learningkerasdeep-learning

ValueError: Exception encountered when calling layer "sequential_5" (type Sequential)


I am following this course : TensorFlow Developer Certificate in 2022: Zero to Mastery

This is the following code :


# 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)


I am getting the following error : Image of the error

Why am I getting the following error and how can I solve it?


Solution

  • You just have to add a second dimension to your data. It has to be (batch_size, features). You could use np.expand_dims to change your inputs from (batch_size,) to (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)