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?
This is what you need to do:
predicted_labels = np.argmax(predict, 0)
For further clarification, refer to this answer: