CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasconv-neural-network
the_begging_beginner
the_begging_beginner

Reputation: 179

Why is my accuracy and loss, 0.000 and nan, in keras?

My understanding is that "sparse_categorical_crossentropy" fits my multi-classification without one-hot-encoding case. I also slowed the adam learning rate in case it is overshooting the predictions.

I am not sure what I am not understanding that I am doing incorrectly.

My input data looks similar to this: enter image description here

My output prediction results are labels: [1 2 3 4 5 6 7 8 9 10] (not one-hot-encoded). Each number represents I want the network to end up choosing.

print(x_train.shape)
print(x_test.shape)
x_train = x_train.reshape(x_train.shape[0], round(x_train.shape[1]/5), 5)
x_test = x_test.reshape(x_test.shape[0], round(x_test.shape[1]/5), 5)
print(x_train.shape)
print(np.unique(y_train))
print(len(np.unique(y_train)))

input_shape = (x_train.shape[1], 5)

adam = keras.optimizers.Adam(learning_rate=0.0001)

model = Sequential()
model.add(Conv1D(512, 5, activation='relu', input_shape=input_shape))
model.add(Conv1D(512, 5, activation='relu'))
model.add(MaxPooling1D(3))
model.add(Conv1D(512, 5, activation='relu'))
model.add(Conv1D(512, 5, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='sparse_categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
    
model.fit(x_train, y_train, batch_size=32, epochs=25, validation_data=(x_test, y_test))
print(model.summary())

Here is the error results: enter image description here

Model Layers (if it helps):

enter image description here

Upvotes: 0

Views: 453

Answers (1)

Marco Cerliani
Marco Cerliani

Reputation: 22031

I see two main problems in your approach

your labels are from 1 to 10... they must start from 0 in order to have them in the range 0-9. this can be achieved simply doing y_train-1 and y_test-1 (if y_test and y_train are numpy arrays)

the last layer of your network must be Dense(10, activation='softmax') where 10 is the number of class to predict and softmax is used to generate probabilities in multiclass problem

Use sparse_categorical_crossentropy is ok because you have integer encoded target

Upvotes: 1

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