How to use Keras' multi layer perceptron for multi-class classification
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This is a pretty common beginner's mistake with Keras. Unlike other Deep Learning frameworks, Keras does not use integer labels for the usual crossentropy loss, instead it expects a binary vector (called "one-hot"), where the vector is just 0's and a 1 over the index of the right class.
You can easily convert your labels to this format with the following code:
from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
Before model.fit. An alternative is to change the loss to "sparse_categorical_crossentropy", which does expect integer labels.
Author by
Danf
Updated on June 15, 2022Comments
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Danf almost 2 years
I tried to follow the instruction here, where it stated that it uses Reuter dataset.
from keras.datasets import reuters (X_train, y_train), (X_test, y_test) = reuters.load_data(path="reuters.pkl", nb_words=None, skip_top=0, maxlen=None, test_split=0.1) from keras.models import Sequential from keras.layers import Dense, Dropout, Activation from keras.optimizers import SGD model = Sequential() # Dense(64) is a fully-connected layer with 64 hidden units. # in the first layer, you must specify the expected input data shape: # here, 20-dimensional vectors. model.add(Dense(64, input_dim=20, init='uniform')) model.add(Activation('tanh')) model.add(Dropout(0.5)) model.add(Dense(64, init='uniform')) model.add(Activation('tanh')) model.add(Dropout(0.5)) model.add(Dense(10, init='uniform')) model.add(Activation('softmax')) sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) #breaks here model.fit(X_train, y_train, nb_epoch=20, batch_size=16) score = model.evaluate(X_test, y_test, batch_size=16)
But the code breaks on model fitting. How can I resolve the issue?
Update: This is the error I got.
In [21]: model.fit(X_train, y_train, ....: nb_epoch=20, ....: batch_size=16) --------------------------------------------------------------------------- Exception Traceback (most recent call last) <ipython-input-21-4b227e56e5a9> in <module>() 1 model.fit(X_train, y_train, 2 nb_epoch=20, ----> 3 batch_size=16) //anaconda/lib/python2.7/site-packages/keras/models.pyc in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs) 400 shuffle=shuffle, 401 class_weight=class_weight, --> 402 sample_weight=sample_weight) 403 404 def evaluate(self, x, y, batch_size=32, verbose=1, //anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight) 969 class_weight=class_weight, 970 check_batch_dim=False, --> 971 batch_size=batch_size) 972 # prepare validation data 973 if validation_data: //anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_dim, batch_size) 909 in zip(y, sample_weights, class_weights, self.sample_weight_modes)] 910 check_array_lengths(x, y, sample_weights) --> 911 check_loss_and_target_compatibility(y, self.loss_functions, self.internal_output_shapes) 912 if self.stateful and batch_size: 913 if x[0].shape[0] % batch_size != 0: //anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in check_loss_and_target_compatibility(targets, losses, output_shapes) 182 if y.shape[1] == 1: 183 raise Exception('You are passing a target array of shape ' + str(y.shape) + --> 184 ' while using as loss `categorical_crossentropy`. ' 185 '`categorical_crossentropy` expects ' 186 'targets to be binary matrices (1s and 0s) ' Exception: You are passing a target array of shape (10105, 1) while using as loss `categorical_crossentropy`. `categorical_crossentropy` expects targets to be binary matrices (1s and 0s) of shape (samples, classes). If your targets are integer classes, you can convert them to the expected format via: ``` from keras.utils.np_utils import to_categorical y_binary = to_categorical(y_int) ``` Alternatively, you can use the loss function `sparse_categorical_crossentropy` instead, which does expect integer targets.
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Danf almost 8 yearsThanks. But I still get the same error message. Here is my full code after incorporating your comment.