Hyper parameters with GridSearch

Building a better model involves iteration and tuning these hyperparameters.
Grid search uses cross validation, splitting the training data up into folds for training and testing of each hyperparameter combination. After all hyperparameter variants are trained, the original test data is used to validate the final model.

GridSearchCV takes a dictionary that describes the parameters that should be tried and a model to train. The grid of parameters is defined as a dictionary, where the keys are the parameters and the values are the settings to be tested.

from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from keras.wrappers.scikit_learn import KerasClassifier
from keras.models import Sequential
import time

def dense_model(units, dropout):
    model = Sequential()
    model.add(Dense(units, activation='relu', input_shape=(28, 28,)))
    model.add(Dropout(dropout))
    model.add(Dense(units, activation='relu'))
    model.add(Dropout(dropout))
    model.add(Flatten())
    model.add(Dense(10, activation='softmax'))
    model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
    return model

hyperparameters = {
    'epochs': [1],
    'batch_size': [64],
    'units': [32, 64, 128],
    'dropout': [0.1, 0.2, 0.4]
}

model = KerasClassifier(build_fn=dense_model, verbose=0)
start = time.clock()
grid = GridSearchCV(estimator=model, param_grid=hyperparameters, cv=6, verbose=4)
grid_result = grid.fit(x_train, y_train)
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))

y_true, y_pred = np.argmax(y_test, axis=1), grid.predict(x_test)
print()
print(classification_report(y_true, y_pred))
print()
print(time.clock() - start)