I created a CIFAR10 dataset learning model using a CNN model. Why is there an error? How should I fix it? I did it in Google colab environment.
import tensorflow as tf import keras from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense from keras.datasets import cifar10 LOSS = 'categorical_crossentropy' OPTIMIZER = 'adam' def model_build(): model = Sequential() # 1 model.add(Conv2D( filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(32,32,3), kernel_regularizer='l2', )) model.add(MaxPooling2D( pool_size=(2,2), padding='same' )) # 2 model.add(Conv2D( filters=64, kernel_size=(5,5), padding='same', activation='relu', kernel_regularizer='l2', )) model.add(MaxPooling2D( pool_size=(2,2), padding='same' )) # 3 model.add(Flatten()) model.add(Dense( units=512, activation='relu', kernel_regularizer='l2', )) # 4 model.add(Dense( units=10, activation='softmax' )) model.compile( loss=LOSS, optimizer=OPTIMIZER, metrics=['accuracy'] ) return model def load_dataset(): (X_train, Y_train), (X_test, Y_test) = cifar10.load_data() X_train = X_train.astype('float32') X_test = X_test.astype('float32') X_train = X_train / 255.0 X_test = X_test / 255.0 return (X_train, Y_train), (X_test, Y_test) model = model_build() (X_train, Y_train), (X_test, Y_test) = load_dataset() model.fit( x=X_train, y=Y_train, epochs=10, batch_size=32, verbose=1, ) model.evaluate( x=X_test, y=Y_test, verbose=1, )
This error occurred to me
ValueError Traceback (most recent call last) in ()
77 epochs=10, 78 batch_size=32, 79 verbose=1, <------Error 80 ) 81
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except 976 if hasattr(e, "ag_error_metadata"): 977 raise e.ag_error_metadata.to_exception(e) <---Error 978 else: 979 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function * return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step ** outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:756 train_step y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:203 __call__ loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:152 __call__ losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:256 call ** return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:1537 categorical_crossentropy return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:4833 categorical_crossentropy target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py:1134 assert_is_compatible_with raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 1) and (None, 10) are incompatible
Thank you for your answering.
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Answer
I think that your labels are integers not one-hot vectors and its shape is (None, 1).
Try:
LOSS = 'sparse_categorical_crossentropy'