Input: a tensorflow dataset with 17000 items:

<_TensorSliceDataset element_spec=(TensorSpec(shape=(2000, 8), dtype=tf.float32, name=None), TensorSpec(shape=(6,), dtype=tf.float32, name=None))>

I receive errors, when I want to train a model. The definition with the model starts with

    inp = tf.keras.Input(shape=(2_000,8))

when I call

history = model.fit(dataset, verbose=VERBOSE,

The Error comes:

ValueError: Input 0 of layer "model_1" is incompatible with the layer: expected shape=(None, 2000, 8), found shape=(2000, 8)

What is the difference between (None, 2000, 8) and (2000, 8)? Why do these two not fit together?

How would I tune the model to expect the shape (2000, 8) ? What would I have to do to the dataset to deliver the shape (None, 2000, 8) ?

Here is some code to reproduce the error:

import keras
import tensorflow as tf
import numpy as np

def create_dataset(items=100, features=8, datapoints=20, classes = 6):
   xdata = np.zeros((items, datapoints, features), dtype="float16")
   ydata = np.zeros((items, classes), dtype="float16")
   for i in range(items):
       xdata[i, :, :] = np.random.rand(datapoints, features)
       ydata[i, :] = np.random.rand(classes)
   return tf.data.Dataset.from_tensor_slices((xdata, ydata))

ds = create_dataset()

def in_block(x, filters, kernel_size):
   return tf.keras.layers.Conv1D(filters = filters,
              kernel_size = 1,
              padding = 'same')(x)
def build_toy_model(datapoints, features, classes):
   # INPUT 
   inp = tf.keras.Input(shape=(datapoints,features))
   inp2 = tf.keras.Input(shape=(datapoints,1))
   x = in_block(inp2, 8, 3)
   x = in_block(x, 16, 3)
   model2 = tf.keras.Model(inputs=inp2, outputs=x)
   all_chains = [model2(inp[:, :, i]) for i in range(features)]
   y = tf.keras.layers.Concatenate()(all_chains)
   y = tf.keras.layers.Dense(64,activation='softmax', dtype='float32')(y)
   y = tf.keras.layers.Dense(classes,activation='softmax', dtype='float32')(y)
   model = tf.keras.Model(inputs=inp, outputs=y)
   opt = tf.keras.optimizers.Adam(learning_rate = 1e-3)
   loss = tf.keras.losses.KLDivergence()
   model.compile(loss=loss, optimizer = opt)
   return model

model = build_toy_model(features=8, datapoints=20, classes = 6)


Edit: See Kaggle


2 Answers 2


In short. This error disappears if you call


See this post: How to use tensorflow dataset to fit a tf.keras model? why do I need to batch?

  • $\begingroup$ Links may not last forever, can you summarize the point you want the link to make in your answer? $\endgroup$
    – m13op22
    Feb 27 at 17:46

(None, 2000, 8) has an extra dimension, with the first dimension being the number of samples/observations. As can be seen in the documentation, the shape argument of tf.keras.Input needs to be the shape of the data excluding the batch size. In your case, this would be (8,).

  • $\begingroup$ so I have 17000 observation and each observation has the format (2000, 8). The Input of the Neural net also should have (2000, 8). I think that it relates to how to put the dataset into the fit method. In general it should work. It did work via a Simple Pipeline. But I am not happy with the documentation of those... tensorflow.org/ranking/api_docs/python/tfr/keras/pipeline/… $\endgroup$
    – Fabi
    Feb 15 at 4:20
  • $\begingroup$ As this answer on stackoverflow mentions, you need to get a batch of data from the dataset which you then pass to fit. $\endgroup$
    – Oxbowerce
    Feb 15 at 17:54

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