What to expect from model.predict in Keras?

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I am new to Keras and trying to write my first code. I want to understand what 'model.predict' should return. Consider a simple model below.

model = keras.Sequential()
model.add(keras.layers.Dense(12, input_dim=232, activation='relu'))
model.add(keras.layers.Dense(232, activation='relu'))
model.add(keras.layers.Dense(1, activation='sigmoid'))
# compile the keras model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit the keras model on the dataset
model.fit(vSignal, vLabels, epochs=15, batch_size=100 )
# evaluate the keras model
_, accuracy = model.evaluate(vSignal, vLabels)
print('Accuracy: %.2f' % (accuracy*100))

pred=model.predict(vSignalT)

Consider we train the "model" with "vSignal" and "vLabels" as shown above. Now consider that the accuracy of the model as given by model.evaluate is 100%. Now if we give same data 'vSignal' to 'model.predict' should we get the 'vLabels' return?

keras
1 Answers

pred=model.predict(vSignalT) returns a numpy arrays of predictions. each row consists of one of the vlabels that the model predicted. for more information refer to here

save return value of fit function:

hist = model.fit(vSignal, vLabels, epochs=15, batch_size=100 );

then check the

hist.history["accuracy"]
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