python - Keras - Error when checking target -


given following code:

import matplotlib.pyplot plt import numpy keras import callbacks keras import optimizers keras.layers import dense, dropout keras.models import sequential keras.callbacks import modelcheckpoint sklearn.preprocessing import standardscaler sklearn.ensemble import extratreesclassifier  sklearn.utils import shuffle  # stopping - stop training before overfitting early_stop = callbacks.earlystopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, mode='auto')  # fix random seed reproducibility seed = 42 numpy.random.seed(seed) # load pima indians dataset dataset = numpy.loadtxt("./data/poc.csv",skiprows=1, delimiter=",") # split input (x) , output (y) variables x = dataset[:, 0:14] y = dataset[:, 14:18]  # # standardize features removing mean , scaling unit variance scaler = standardscaler() x = scaler.fit_transform(x)   #adam optimizer learning rate decay opt = optimizers.adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0001)  ## create our model model = sequential()  model.add(dense(200, input_dim=14, kernel_initializer='uniform', activation='relu')) model.add(dropout(0.2)) model.add(dense(100, activation='relu')) model.add(dropout(0.2)) model.add(dense(60, activation='relu')) model.add(dropout(0.2)) model.add(dense(30, activation='relu')) model.add(dropout(0.2)) model.add(dense(5, activation='sigmoid'))   model.summary() # compile model using binary crossentropy since predicting 0/1 model.compile(loss='categorical_crossentropy',               optimizer=opt,               metrics=['accuracy'])  # checkpoint filepath="./checkpoints/weights.best.hdf5" checkpoint = modelcheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=true, mode='max')  # fit model history = model.fit(x, y, validation_split=0.33, epochs=10000, batch_size=10, verbose=0, callbacks=[early_stop,checkpoint])

and data so:

17.6,1,1,0,1,0,0,0,0,0,0,3.9,9.2,20.29,0,1,0,0,0 12.9,1,0,1,0,0,0,0,0,0,0,4.1,13.5,0.08,0,0,0,1,0 3.2,1,0,1,0,0,0,0,0,0,0,4.122031746,13.8,0.01,0,0,0,0,0 ...

i following output / error:

_________________________________________________________________ layer (type)                 output shape              param #    ================================================================= dense_1 (dense)              (none, 200)               3000       _________________________________________________________________ dropout_1 (dropout)          (none, 200)               0          _________________________________________________________________ dense_2 (dense)              (none, 100)               20100      _________________________________________________________________ dropout_2 (dropout)          (none, 100)               0          _________________________________________________________________ dense_3 (dense)              (none, 60)                6060       _________________________________________________________________ dropout_3 (dropout)          (none, 60)                0          _________________________________________________________________ dense_4 (dense)              (none, 30)                1830       _________________________________________________________________ dropout_4 (dropout)          (none, 30)                0          _________________________________________________________________ dense_5 (dense)              (none, 5)                 155        ================================================================= total params: 31,145 trainable params: 31,145 non-trainable params: 0 _________________________________________________________________

error when checking target: expected dense_5 have shape (none, 1) got array shape (716, 4)

what missing?

your last layer, dense_5, has size 5, while target has size 4.

in order work, size of each targert must number of classes want predict. remember have represented in 1 hot enconding. can use to_categorical keras.


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