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# Tuning Neural Network Hyperparameters

## Preliminaries

```
/* Load libraries */
import numpy as np
from keras import models
from keras import layers
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import make_classification
/* Set random seed */
np.random.seed(0)
```

```
Using TensorFlow backend.
```

## Generate Target And Feature Data

```
/* Number of features */
number_of_features = 100
/* Generate features matrix and target vector */
features, target = make_classification(n_samples = 10000,
n_features = number_of_features,
n_informative = 3,
n_redundant = 0,
n_classes = 2,
weights = [.5, .5],
random_state = 0)
```

## Create Function That Constructs A Neural Network

```
/* Create function returning a compiled network */
def create_network(optimizer='rmsprop'):
/* Start neural network */
network = models.Sequential()
/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=16, activation='relu', input_shape=(number_of_features,)))
/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=16, activation='relu'))
/* Add fully connected layer with a sigmoid activation function */
network.add(layers.Dense(units=1, activation='sigmoid'))
/* Compile neural network */
network.compile(loss='binary_crossentropy', # Cross-entropy
optimizer=optimizer, # Optimizer
metrics=['accuracy']) # Accuracy performance metric
/* Return compiled network */
return network
```

## Wrap Function In KerasClassifier

```
/* Wrap Keras model so it can be used by scikit-learn */
neural_network = KerasClassifier(build_fn=create_network, verbose=0)
```

## Create Hyperparameter Search Space

```
/* Create hyperparameter space */
epochs = [5, 10]
batches = [5, 10, 100]
optimizers = ['rmsprop', 'adam']
/* Create hyperparameter options */
hyperparameters = dict(optimizer=optimizers, epochs=epochs, batch_size=batches)
```

## Conduct Grid Search

```
/* Create grid search */
grid = GridSearchCV(estimator=neural_network, cv=3, param_grid=hyperparameters)
/* Fit grid search */
grid_result = grid.fit(features, target)
```

## Find Best Model’s Hyperparameters

```
/* View hyperparameters of best neural network */
grid_result.best_params_
```

`{'batch_size': 10, 'epochs': 5, 'optimizer': 'adam'}`

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