Modify multiclass predictions array of Sequential model to match KerasClassifier

Question

I understand that using

dataframe = pandas.read_csv("IrisDataset.csv", header=None)
dataset = dataframe.values 
X = dataset[:,0:4].astype(float)
Y = dataset[:,4]

# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one hot encoded)
dummy_y = np_utils.to_categorical(encoded_Y)

def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(8, input_dim=4, activation='relu'))
    model.add(Dense(3, activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam',  metrics=['accuracy'])
  return model
estimator = KerasClassifier(build_fn=baseline_model, epochs=50, batch_size=5, verbose=0)    

estimator.fit(X, dummy_y)
predictions=estimator.predict(X)

to create the predictions, metrics can be calculated by

print "PRECISION\t", precision_score(Y,encoder.inverse_transform(predictions), average=None)

where Y is the labels of the training set. But if instead of the estimator, I use this:

model = Sequential()
model.add(Dense(8, input_dim=4, activation='relu'))
model.add(Dense(3, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam',  metrics=['accuracy']
model.fit(X, dummy_y,epochs=50,batch_size=5,  shuffle=True, verbose=1)
predictions=model.predict(x=tst_X,batch_size=50,verbose=1) 

then predictions has different form and I can't use it as a parameter for the calculations. Is there another way to calculate precision and other metrics? Do I need to transform predictions?

Answer 1

The output of your Sequential model will have the shape (3,), containing the estimated class probabilities. Next, you have to get the predicted (most-likely) class for each prediction, i.e. you have to take the argmax

predictions = model.predict(x=tst_X, batch_size=50, verbose=1)
predictions = np.argmax(predictions, 1)

Then, you can use the rest of the code, just as it is.

Otherwise, you could also use the predict_classes function of the Sequential model directly, which is basically doing the same thing:

predictions = model.predict_classes(x=tst_X, batch_size=50, verbose=1)