Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error **alueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [8, 28, 28]** in** Python**. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

Table of Contents

## How alueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [8, 28, 28] Error Occurs?

Today I get the following error **alueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [8, 28, 28]** in** Python**.

## How To Solve alueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [8, 28, 28] Error ?

**How To Solve alueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [8, 28, 28] Error ?**To Solve alueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [8, 28, 28] Error This means that you have to reshape your training set with .reshape(n_images, 286, 384, 1). Now you have added an extra dimension without changing the data and your model is ready to run.

## Solution 1

The input layers of the model you created needs a 4 dimension tensor to work with but the x_train tensor you are passing to it has only 3 dimensions

This means that you have to reshape your training set with .reshape(n_images, 286, 384, 1). Now you have added an extra dimension without changing the data and your model is ready to run.

you need to reshape your x_train tensor to a 4 dimension before training your model. for example:

x_train = x_train.reshape(-1, 28, 28, 1)

## Solution 2

You need to add a channel dimension. Keras expects this data format:

(n_samples, height, width, channels)

For instance this, if your images are greyscale, they have 1 channel, and so they need to be given to Keras in this format:

(60000, 28, 28, 1)

Unfortunately, grayscale pictures will often be given/downloaded without a channel dimension, for instance in `tf.keras.datasets.mnist.load_data`

, which will be `(60000, 28, 28)`

, which is problematic.

### Solution:

You can use `tf.expand_dims`

to add a dimension

xtrain = tf.expand_dims(xtrain, axis=-1)

Now your input shape will be:

(60000, 28, 28, 1)

There are other alternatives that do the same:

xtrain = xtrain[..., np.newaxis] xtrain = xtrain[..., None] xtrain = xtrain.reshape(-1, 28, 28, 1) xtrain = tf.reshape(xtrain, (-1, 28, 28, 1)) xtrain = np.expand_dims(xtrain, axis=-1)

**Summery**

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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