TypeError : Inputs to a layer should be tensor-CodePudding

I am new to deep learning currently trying to learn neural network.However,I encountered this problem while training the neural network.

This is the input .I thought by using the tensor Dataset I am ready to pass the values into the model I build.

train_dataset = tf.data.Dataset.from_tensor_slices((train.values, trainLabel.values))
test_dataset = tf.data.Dataset.from_tensor_slices((test.values, testLabel.values))
cv_dataset = tf.data.Dataset.from_tensor_slices((val.values, valLabel.values))

for features, targets in train_dataset.take(5):
  print ('Features: {}, Target: {}'.format(features, targets))


Features: [ 0 40  0  0  0  1 31 33 17], Target: 29
Features: [ 0 32  0  1  0  1 50 55 44], Target: 7
Features: [ 0 32  1  0  1  1 12 43 31], Target: 34
Features: [ 0 29  1  1  1  0 56 52 37], Target: 14
Features: [ 0 25  0  0  1  1 29 30 15], Target: 17

This is my model using Keras API:

model = tf.keras.Sequential([
  tf.keras.layers.Dense(10, activation=tf.nn.relu, input_shape=(9,)),  # input shape required
  tf.keras.layers.Dense(10, activation=tf.nn.relu),
  tf.keras.layers.Dense(3)
])

I am trying to preview the output before training the neural network.

predictions = model(train_dataset)
predictions[:5]

However, I got this error :

TypeError: Inputs to a layer should be tensors. Got: <BatchDataset element_spec=(TensorSpec(shape=(None, 9), dtype=tf.int64, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>

CodePudding user response：

You are trying to feed your model a dataset object while it expects a batch of samples. You should use .as_numpy_iterator, then .next to get one batch of data.

train_dataset = tf.data.Dataset.from_tensor_slices((train.values, trainLabel.values))
train_iterator = train_dataset.as_numpy_iterator()

one_batch = train_iterator.next()

model(one_batch)

The model expects a batch of input (that is a 2d numpy array, where the first dimension is the size of the batch, and the second dimension is the input size, which is 9 in your case), this is the meaning of None when you look at what is printed by model.summary().

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 10)                100       
_________________________________________________________________
dense_1 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_2 (Dense)              (None, 3)                 33        
=================================================================
Total params: 243
Trainable params: 243
Non-trainable params: 0
_________________________________________________________________

See https://www.tensorflow.org/api_docs/python/tf/data/Dataset