## Introduction to Keras Model Predict

Keras model predicts is the method of function provided in Keras that helps in the predictions of output depending on the specified samples of input to the model. In this article, we will get the knowledge about Keras model predict by studying its subtopics which include What is the keras model predicts, Using keras model predict Compilation, How to use the keras model predicts, keras model predict examples, and Conclusion about the same.

### What is Keras model predict?

The prediction requires the model to perform certain computations which are carried out in batches. If we have input on a large scale then only we can make use of this method efficiently while for the inputs that are small called and which can accommodate and fit in a single batch, for them we can simply give the direct use of __call__() function which increases the speed of execution in this case.

One of the examples is when we have the layers inside the model which behave differently when they are in the inference mode such as the Batch normalization layer when the model(sample, training = false) and model(sample) are used. As in the case of dropout and noise, the regularization layers do not affect the test loss.

### Using Keras model predict Compilation

We will have to compile the Keras model by using the method of Keras called compile() which takes the arguments of Keras loss function, optimizer, metrics, and other optional specifications.

Before we fit the model for the training and prediction purpose, we will have to compile it by using the compile() method.

### How to use keras model predict?

The definition of the keras predict function method is as shown below –

`Predict (sample, batch_size = None, callbacks = None, verbose = 0, max_queue_size = 10, steps = None, use_multiprocessing = false, workers = 1)`

The arguments and parameters used in the above syntax are described in detail below –

- Sample – It is the samples of input that can either have the value in TensorFlow tensor, NumPy array, or array-like or list of arrays or the dataset of tf. data, an instance of keras.utils.Sequence or a generic data format. All these specified types depend on whether we have single or multiple inputs for the model and for the purpose of iterator or unpacking behavior that are further useful for fitting the model during training.
- Verbose – It specifies the mode of verbose which can either have 1 or 0 values.
- Batch_size – It is an integer parameter that can even have None as a value when not to be specified. It helps in the representation of the count of the samples per batch. The default value when not specified corresponds to 32. When we are going to use the data which has the format of the dataset, it is not necessary to specify the size of the batch which stands true for instances of keras.utils.Sequence or generators.
- Callbacks – List of instances of the class keras.callbacks.Callback is applied in the phase of prediction.
- Steps – It is the count of batches or steps which are declared even before the round of prediction is completed. When not specified has a default value of None.
- Workers – It is an integer value for the input of keras. utils.Sequence or generator. When the process-based threading is done it spin up to the maximum number of processes. The default value when not specified is 1.
- Max_queue_size – It is an integer value for the input of Keras. utils.Sequence or generator only. The default value when not specified is considered to be 10.
- Use_multiprocessing – It is a Boolean value that is used for the inputs of Keras. utils.Sequence only. When the value is set to true then threading that is process-based is used and when false or unspecified which in turn gets too false as it is the default value.
- The output of the predict function is the NumPy array containing the predictions.
- The errors that might raise include value error and runtime error. Value error is raised when there is mismatch found between the expectations of the model and input data that is provided or when using the model that is stateful in the sample count received is not a multiple of the size of the batch. Runtime error is raised when model.predict is wrapped inside the tensorflow’s tf.function.

### Keras model predict Examples

#### Example #1

# In a regression problem, we will try to make the predictions in this example

`from keras.sampleEducbaModels import Sequential`

from keras.laYvalueers import Dense

from sklearn.datasets import make_regression

from sklearn.preprocessing import MinMaxScaler

# data set genereation for regression

Xvalue, Yvalue = make_regression(n_samples=100, n_features=2, noise=0.1, random_state=1)

xScalarValue, YScalarValue = MinMaxScaler(), MinMaxScaler()

xScalarValue.fit(Xvalue)

YScalarValue.fit(Yvalue.reshape(100,1))

Xvalue = xScalarValue.transform(Xvalue)

Yvalue = YScalarValue.transform(Yvalue.reshape(100,1))

# final sampleEducbaModel is defined and then fitted

sampleEducbaModel = Sequential()

sampleEducbaModel.add(Dense(4, input_dim=2, activation='relu'))

sampleEducbaModel.add(Dense(4, activation='relu'))

sampleEducbaModel.add(Dense(1, activation='linear'))

sampleEducbaModel.compile(loss='mse', optimizer='adam')

sampleEducbaModel.fit(Xvalue, Yvalue, epochs=1000, verbose=0)

# This will be the new instances where we are unknown about the outcomes

Xvaluenew, a = make_regression(n_samples=3, n_features=2, noise=0.1, random_state=1)

Xvaluenew = xScalarValue.transform(Xvaluenew)

# predictions are made

Yvaluenew = sampleEducbaModel.predict(Xvaluenew)

# Display the outcomes of prediction and input values

for i in range(len(Xvaluenew)):

print("Xvalue=%s, Value That Is Predicted=%s" % (Xvaluenew[i], Yvaluenew[i]))

The output of the execution of the above code snippet gives us the following prediction results –

#### Example #2

# In a regression problem, we will trYvalue to make the predictions in this example

`from keras.sampleEducbaModel2s import Sequential`

from keras.layValueers import Dense

from sklearn.datasets import make_regression

from sklearn.preprocessing import MinMaxScaler

from numpyValue import arrayValue

# data set genereation for regression

xValue, yValue = make_regression(n_samples=100, n_features=2, noise=0.1, random_state=1)

scalarxValue, scalarY = MinMaxScaler(), MinMaxScaler()

scalarxValue.fit(xValue)

scalarY.fit(yValue.reshape(100,1))

xValue = scalarxValue.transform(xValue)

yValue = scalarY.transform(yValue.reshape(100,1))

# final sampleEducbaModel is defined and then fitted

sampleEducbaModel2 = Sequential()

sampleEducbaModel2.add(Dense(4, input_dim=2, activation='relu'))

sampleEducbaModel2.add(Dense(4, activation='relu'))

sampleEducbaModel2.add(Dense(1, activation='linear'))

sampleEducbaModel2.compile(loss='mse', optimizer='adam')

sampleEducbaModel2.fit(xValue, yValue, epochs=1000, verbose=0)

# This will be the new instances where we are unknown about the outcomes

xValuenew = arrayValue([[0.29466096, 0.30317302]])

# predict the value

yValuenew = sampleEducbaModel2.predict(xValuenew)

# This will be the new instances where we are unknown about the outcomes

print("xValue=%s, Value That Is Predicted = %s" % (xValuenew[0], yValuenew[0]))

The output of the execution of the above program is as shown below –

Note that in both the above examples, the results may vary when you will go for execution due to various factors such as the procedure of evaluation, the nature of the algorithm, and the numerical precision differences.

### Conclusion

Keras model predict is the method available in keras that help us predict the outputs by performing various computations that are carried out in batches.

### Recommended Articles

This is a guide to Keras Model Predict. Here we discuss the Introduction, What is Keras model predict, examples with code implementation. You may also look at the following articles to learn more –