ValueError: Error when checking target: expected dense_8 to have shape (10,) but got array with shape (95,)

Question

I'm trying to solve this. I'm working in CNN algorithm to process the image, and recognize the image and then I am facing some error like this.....

ValueError: Error when checking target: expected dense_8 to have shape (10,) but got an array with shape (95,)

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_6 (Conv2D)            (None, 126, 126, 32)      896       
_________________________________________________________________
batch_normalization_5 (Batch (None, 126, 126, 32)      128       
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 63, 63, 32)        0         
_________________________________________________________________
dropout_5 (Dropout)          (None, 63, 63, 32)        0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 61, 61, 64)        18496     
_________________________________________________________________
batch_normalization_6 (Batch (None, 61, 61, 64)        256       
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 30, 30, 64)        0         
_________________________________________________________________
dropout_6 (Dropout)          (None, 30, 30, 64)        0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 28, 28, 128)       73856     
_________________________________________________________________
batch_normalization_7 (Batch (None, 28, 28, 128)       512       
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 14, 14, 128)       0         
_________________________________________________________________
dropout_7 (Dropout)          (None, 14, 14, 128)       0         
_________________________________________________________________
flatten_3 (Flatten)          (None, 25088)             0         
_________________________________________________________________
dense_7 (Dense)              (None, 512)               12845568  
_________________________________________________________________
batch_normalization_8 (Batch (None, 512)               2048      
_________________________________________________________________
dropout_8 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_8 (Dense)              (None, 10)                5130      
=================================================================
Total params: 12,946,890
Trainable params: 12,945,418
Non-trainable params: 1,472
_________________________________________________________________
Found 48905 images belonging to 95 classes.
Found 16421 images belonging to 95 classes.
{'Apple Braeburn': 0, 'Apple Golden 1': 1, 'Apple Golden 2': 2, 'Apple Golden 3': 3, 'Apple Granny Smith': 4, 'Apple Red 1': 5, 'Apple Red 2': 6, 'Apple Red 3': 7, 'Apple Red Delicious': 8, 'Apple Red Yellow 1': 9, 'Apple Red Yellow 2': 10, 'Apricot': 11, 'Avocado': 12, 'Avocado ripe': 13, 'Banana': 14, 'Banana Lady Finger': 15, 'Banana Red': 16, 'Cactus fruit': 17, 'Cantaloupe 1': 18, 'Cantaloupe 2': 19, 'Carambula': 20, 'Cherry 1': 21, 'Cherry 2': 22, 'Cherry Rainier': 23, 'Cherry Wax Black': 24, 'Cherry Wax Red': 25, 'Cherry Wax Yellow': 26, 'Chestnut': 27, 'Clementine': 28, 'Cocos': 29, 'Dates': 30, 'Granadilla': 31, 'Grape Blue': 32, 'Grape Pink': 33, 'Grape White': 34, 'Grape White 2': 35, 'Grape White 3': 36, 'Grape White 4': 37, 'Grapefruit Pink': 38, 'Grapefruit White': 39, 'Guava': 40, 'Hazelnut': 41, 'Huckleberry': 42, 'Kaki': 43, 'Kiwi': 44, 'Kumquats': 45, 'Lemon': 46, 'Lemon Meyer': 47, 'Limes': 48, 'Lychee': 49, 'Mandarine': 50, 'Mango': 51, 'Mangostan': 52, 'Maracuja': 53, 'Melon Piel de Sapo': 54, 'Mulberry': 55, 'Nectarine': 56, 'Orange': 57, 'Papaya': 58, 'Passion Fruit': 59, 'Peach': 60, 'Peach 2': 61, 'Peach Flat': 62, 'Pear': 63, 'Pear Abate': 64, 'Pear Kaiser': 65, 'Pear Monster': 66, 'Pear Williams': 67, 'Pepino': 68, 'Physalis': 69, 'Physalis with Husk': 70, 'Pineapple': 71, 'Pineapple Mini': 72, 'Pitahaya Red': 73, 'Plum': 74, 'Plum 2': 75, 'Plum 3': 76, 'Pomegranate': 77, 'Pomelo Sweetie': 78, 'Quince': 79, 'Rambutan': 80, 'Raspberry': 81, 'Redcurrant': 82, 'Salak': 83, 'Strawberry': 84, 'Strawberry Wedge': 85, 'Tamarillo': 86, 'Tangelo': 87, 'Tomato 1': 88, 'Tomato 2': 89, 'Tomato 3': 90, 'Tomato 4': 91, 'Tomato Cherry Red': 92, 'Tomato Maroon': 93, 'Walnut': 94}
Epoch 1/10
Traceback (most recent call last):
ValueError: Error when checking target: expected dense_8 to have shape (10,)
but got array with shape (95,)

Answer 1

The last layer of a model should contain nodes equal to the number of classes in your data. You have 95 classes, so the last layer should have 95 nodes

Change the last layer of your model to,

model.add(Dense(95, activation='softmax'))