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I am currently creating a CNN where the main task the network has is to classify input information into different classes. These classes are exact values of the predicted frequencies.

This is what I have built so far:

def evaluate_model(X_train, Y_train, X_test, Y_test,n_filters):
    verbose, epochs, batch_size = 1, 10, 3
    n_timesteps, n_features = X_train.shape[1], X_train.shape[2]
    model = Sequential()
    model.add(Conv1D(filters=n_filters, kernel_size=8, activation='relu', input_shape=(n_timesteps,n_features)))
    model.add(Conv1D(filters=n_filters, kernel_size=8, activation='relu'))
    model.add(Dropout(0.5))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(100, activation='relu'))
    model.add(Dense(50, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    print(model.summary())
    # fit network
    history=model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=verbose)
    # evaluate model
    _, accuracy = model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=1) 
    return accuracy, model

predict=model.predict(amplitude_t)
print(predict)

I am trying to predict the values of some new signals that I created which works perfectly. Although my output is a probability output and I want to convert this back into the actual frequency values. Is there a way to do this?

Pamela Treacy
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  • Please notice that SO does *not* work by simply [throwing all our code here as-is](http://idownvotedbecau.se/toomuchcode/); please use some dummy data to post a [mre] *specifically* on your issue. – desertnaut Jul 26 '21 at 11:15

1 Answers1

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This is what you need to do:

predicted_labels = np.argmax(predict, 0)

For further clarification, refer to this answer:

https://stackoverflow.com/a/52361283/7185112

Syed Waleed Hyder
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