Exception encountered when calling layer "sequential

This is a simple code for creating,compile and fitting a model for a single layer

X = tf.cast(tf.constant(X),dtype=tf.float32)
y = tf.cast(tf.constant(y),dtype=tf.float32)

#Set Random seed

tf.random.set_seed(42)

#1.create a model using the Sequential API

model = tf.keras.Sequential([
                             tf.keras.layers.Dense(1)
                             ])

#2.Compile the model

model.compile(loss=tf.keras.losses.mae,
              optimizer=tf.keras.optimizers.SGD(),metrics=["mae"])

#Fit the model

model.fit(X,y,epochs = 5)

but I am getting this error at the end.

ValueError: in user code:

    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 878, in train_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 867, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in run_step  **
        outputs = model.train_step(data)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 808, in train_step
        y_pred = self(x, training=True)
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 227, in assert_input_compatibility
        raise ValueError(f'Input {input_index} of layer "{layer_name}" '

    ValueError: Exception encountered when calling layer "sequential_6" (type Sequential).
    
    Input 0 of layer "dense_7" is incompatible with the layer: expected min_ndim=2, found ndim=1. Full shape received: (None,)
    
    Call arguments received:
      • inputs=tf.Tensor(shape=(None,), dtype=float64)
      • training=True
      • mask=None

Why Sequential_6 and dense_7??? This is a single layer.

CodePudding user response：

You are forgetting the batch dimension. Your input to your model has to have the shape (batch_size, features). Try something like this:


X = tf.cast(tf.constant([0.5]),dtype=tf.float32)
y = tf.cast(tf.constant([0.6]),dtype=tf.float32)
X = tf.expand_dims(X, axis=0)
y = tf.expand_dims(y, axis=0)
model = tf.keras.Sequential([
                             tf.keras.layers.Dense(1)
                             ])
model.compile(loss=tf.keras.losses.mae,
              optimizer=tf.keras.optimizers.SGD(),metrics=["mae"])

model.fit(X,y,epochs = 5)

Sequential_6 is your model name, dense_7 is the name of the Dense layer. Every time you run your model again, the numbers in the names are incremented.