CODEWITHSUNDEEP
python-3.xtensorflowconv-neural-network
SajanGohil
SajanGohil

Reputation: 969

logits and labels must have the same first dimension, got logits shape [3662,5] and labels shape [18310]

I am trying to create a CNN with tensorflow, my images are 64x64x1 images and I have an array of 3662 images which I am using for training. I have total 5 labels which I have one-hot encoded. I am getting this error everytime:

InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [3662,5] and labels shape [18310]
     [[{{node loss_2/dense_5_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]]

my neural network structure is this:

def cnn_model():
    model = models.Sequential()
#     model.add(layers.Dense(128, activation='relu', ))
    model.add(layers.Conv2D(128, (3, 3), activation='relu',input_shape=(64, 64, 1)))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu',padding = 'same'))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))
    model.add(layers.Dense(64, activation='relu'))
    model.add(layers.Flatten())
    model.add(layers.Dense(5, activation='softmax'))

    model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(),
              metrics=['accuracy'])
    print(model.summary())
    return model

My model summary is this:

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_9 (Conv2D)            (None, 62, 62, 128)       1280      
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 31, 31, 128)       0         
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 31, 31, 64)        73792     
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 15, 15, 64)        0         
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 15, 15, 64)        36928     
_________________________________________________________________
dense_4 (Dense)              (None, 15, 15, 64)        4160      
_________________________________________________________________
flatten_2 (Flatten)          (None, 14400)             0         
_________________________________________________________________
dense_5 (Dense)              (None, 5)                 72005     
=================================================================
Total params: 188,165
Trainable params: 188,165
Non-trainable params: 0

my output array is of the shape (3662,5,1). I have seen other answers to same questions but I can't figure out the problem with mine. Where am I wrong?

Edit: My labels are stored in one hot encoded form using these:

df = pd.get_dummies(df)
diag = np.array(df)
diag = np.reshape(diag,(3662,5,1))

I have tried as numpy array and after converting to tensor(same for input as per documentation)

Upvotes: 0

Views: 941

Answers (1)

gorjan
gorjan

Reputation: 5555

The problem lines within the choice of the loss function tf.keras.losses.SparseCategoricalCrossentropy(). According to what you are trying to achieve you should use tf.keras.losses.CategoricalCrossentropy(). Namely, the documentation of tf.keras.losses.SparseCategoricalCrossentropy() states:

Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided as integers.

On the other hand, the documentation of tf.keras.losses.CategoricalCrossentropy() states:

We expect labels to be provided in a one_hot representation.

And because your labels are encoded as one-hot, you should use tf.keras.losses.CategoricalCrossentropy().

Upvotes: 1

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