Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error **Tensorflow : logits and labels must have the same first dimension** **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 Tensorflow : logits and labels must have the same first dimension Error Occurs?

Today I get the following error **Tensorflow : logits and labels must have the same first dimension** **in python**.

## How To Solve Tensorflow : logits and labels must have the same first dimension Error ?

**How To Solve Tensorflow : logits and labels must have the same first dimension Error ?**To Solve Tensorflow : logits and labels must have the same first dimension Error I resolved it changing from

`sparse_categorical_crossentropy`

to`categorical_crossentropy`

and is now running fine.

**Tensorflow : logits and labels must have the same first dimension**To Solve Tensorflow : logits and labels must have the same first dimension Error I resolved it changing from

`sparse_categorical_crossentropy`

to`categorical_crossentropy`

and is now running fine.

## Solution 1

The problem is in your target shape and is related to the correct choice of an appropriate loss function. you have 2 possibilities:

**1. possibility**: if you have 1D integer encoded target, you can use `sparse_categorical_crossentropy`

as loss function

n_class = 3 n_features = 100 n_sample = 1000 X = np.random.randint(0,10, (n_sample,n_features)) y = np.random.randint(0,n_class, n_sample) inp = Input((n_features,)) x = Dense(128, activation='relu')(inp) out = Dense(n_class, activation='softmax')(x) model = Model(inp, out) model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy']) history = model.fit(X, y, epochs=3)

**2. possibility**: if you have one-hot encoded your target in order to have 2D shape (n_samples, n_class), you can use `categorical_crossentropy`

n_class = 3 n_features = 100 n_sample = 1000 X = np.random.randint(0,10, (n_sample,n_features)) y = pd.get_dummies(np.random.randint(0,n_class, n_sample)).values inp = Input((n_features,)) x = Dense(128, activation='relu')(inp) out = Dense(n_class, activation='softmax')(x) model = Model(inp, out) model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy']) history = model.fit(X, y, epochs=3)

## Solution 2

I resolved it changing from `sparse_categorical_crossentropy`

to `categorical_crossentropy`

and is now running fine.

**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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