Error while training for rock paper scissors

So I’m trying my hands on Convolution Neural Network and thought a more hands-on approach is the way to go with the rock paper scissors dataset

This is how the model has been built

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(filters=32,
                          kernel_size=3,
                          activation="relu",
                          input_shape=(200, 200, 3)),
    tf.keras.layers.Conv2D(32, 3, activation="relu"),
    tf.keras.layers.MaxPooling2D(pool_size=2, padding="valid"),
    tf.keras.layers.Dropout(0.2),
    
    tf.keras.layers.Conv2D(64, 3, activation="relu"),
    tf.keras.layers.MaxPooling2D(pool_size=2, padding="valid"),
    tf.keras.layers.Dropout(0.2),
    
    tf.keras.layers.Conv2D(128, 3, activation="relu"),
    tf.keras.layers.MaxPooling2D(pool_size=2, padding="valid"),
    tf.keras.layers.Dropout(0.2),
    
    tf.keras.layers.Flatten(),
    
    tf.keras.layers.Dense(256, activation="relu"),
    tf.keras.layers.Dropout(0.5),
    
    tf.keras.layers.Dense(3, activation="softmax")
])

And this is the summary

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_6 (Conv2D)            (None, 198, 198, 32)      896       
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 196, 196, 32)      9248      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 98, 98, 32)        0         
_________________________________________________________________
dropout_4 (Dropout)          (None, 98, 98, 32)        0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 96, 96, 64)        18496     
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 48, 48, 64)        0         
_________________________________________________________________
dropout_5 (Dropout)          (None, 48, 48, 64)        0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 46, 46, 128)       73856     
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 23, 23, 128)       0         
_________________________________________________________________
dropout_6 (Dropout)          (None, 23, 23, 128)       0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 67712)             0         
_________________________________________________________________
dense_2 (Dense)              (None, 256)               17334528  
_________________________________________________________________
dropout_7 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 3)                 771       
=================================================================
Total params: 17,437,795
Trainable params: 17,437,795
Non-trainable params: 0
_________________________________________________________________

The model has been compiled as such

model.compile(loss="categorical_crossentropy",
             optimizer=tf.optimizers.Adam(),
             metrics=['accuracy'])

and trained

history = model.fit(train_data, epochs=10, steps_per_epoch=20, validation_data = val_data, validation_steps=5, verbose=1)

But while training I get thrown this error

InvalidArgumentError: Input to reshape is a tensor with 3686400 values, but the requested shape requires a multiple of 67712
[[node sequential_1/flatten_1/Reshape (defined at tmp/ipykernel_17/154995288.py:1) ]] [Op:__inference_train_function_2119]
Function call stack:
train_function

What am I doing wrong? Any help is greatly appreciated

The link says that the data has shape (200,300) but your input shape says (200,200). I don’t know if you resized your input to the latter but you may consider changing it to the appropriate size before training. It may solve your issue.

changing the input shape of the first Conv2D’s input_shape=(300,200,3), resulted in the same error

You mean (200,300,3)?