Input 0 of layer "dense_2" is incompatible with the layer: expected axis -1 of input shape to have value 16384, but received input with shape (None, 65536) about keras HOT 8 CLOSED

@sachinprasadhs help me out

from keras.

Could you please provide reproducible code in a colab Gist to understand more on the issue. Thanks!

from keras.

@sachinprasadhs yeaa

https://colab.research.google.com/drive/1QyLNRMwOhuMpZ_4_eJ5m3pKh2Np_wG7g?usp=sharing

from keras.

You are restricting the input_shape for each convolution operation to be 128,128,3, which does not fit to the actual data size which you are passing which is 256,256,3, replacing your code with below code should work.

from keras.layers import Dense, Conv2D, MaxPool2D, Flatten
from keras.models import Sequential

model = Sequential()

model.add(Conv2D(filters=32, kernel_size=3, padding="same", activation="relu",input_shape=[256,256,3]))
model.add(Conv2D(filters=32, kernel_size=3, padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=2,strides=2))

model.add(Conv2D(filters=64, kernel_size=3, padding="same", activation="relu"))
model.add(Conv2D(filters=64, kernel_size=3, padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=2,strides=2))


model.add(Conv2D(filters=128, kernel_size=3, padding="same", activation="relu"))
model.add(Conv2D(filters=128, kernel_size=3, padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=2,strides=2))

model.add(Conv2D(filters=256, kernel_size=3, padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=3, padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=2,strides=2))

model.add(Flatten())

model.add(Dense(units = 1024, activation="relu"))

model.add(Dense(units = 1024, activation="relu"))

model.add(Dense(units = 38, activation="softmax"))

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

from keras.

history = model.fit(x=training_set, validation_data=validation_set, epochs=10)

ValueError Traceback (most recent call last)
Cell In[12], line 1
----> 1 history = model.fit(x=training_set, validation_data=validation_set, epochs=10)

File /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/keras/src/utils/traceback_utils.py:123, in filter_traceback..error_handler(*args, **kwargs)
120 filtered_tb = _process_traceback_frames(e.traceback)
121 # To get the full stack trace, call:
122 # keras.config.disable_traceback_filtering()
--> 123 raise e.with_traceback(filtered_tb) from None
124 finally:
125 del filtered_tb

File /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/keras/src/backend/tensorflow/nn.py:546, in categorical_crossentropy(target, output, from_logits, axis)
540 raise ValueError(
541 "Arguments target and output must be at least rank 1. "
542 "Received: "
543 f"target.shape={target.shape}, output.shape={output.shape}"
544 )
545 if len(target.shape) != len(output.shape):
--> 546 raise ValueError(
547 "Arguments target and output must have the same rank "
548 "(ndim). Received: "
549 f"target.shape={target.shape}, output.shape={output.shape}"
550 )
551 for e1, e2 in zip(target.shape, output.shape):
552 if e1 is not None and e2 is not None and e1 != e2:

ValueError: Arguments target and output must have the same rank (ndim). Received: target.shape=(None,), output.shape=(None, 38)

still i get the error dude! :(

from keras.

It is basically the number of classes you are using, if the data is a binary classification then you need to change the output layer to something like model.add(Dense(units = 1, activation="sigmoid"))

from keras.

Thank you so much bro solved

from keras.

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from keras.