def random_test_with_dropout_tf():
X, Y, Y_onehot = input_data.loadRandomData()
layer_types = [
'relu',
'softmax',
]
hidden_layer_dims = [
120,
]
parameters = nn_model_tf.model(X,
Y_onehot,
hidden_layer_dims,
layer_types,
learning_rate=0.5,
num_iterations=2001,
num_batches=2,
prob=0.5)
Y_predict, train_accuracy = nn_model_tf.predict(X, Y_onehot, parameters,
hidden_layer_dims,
layer_types)
train_accuracy = np.sum(Y_predict == Y) / Y.shape[1]
print('Training accuracy: %f' % train_accuracy)
plot.show_decision_boundry(X, Y, Y_onehot, nn_model_tf.predict, parameters,
hidden_layer_dims, layer_types)
def random_test_tf():
X, Y, Y_onehot = input_data.loadRandomData()
layer_types = [
'relu',
'softmax',
]
hidden_layer_dims = [
120,
]
parameters = nn_model_tf.model(X,
Y_onehot,
hidden_layer_dims,
layer_types,
learning_rate=0.5,
num_iterations=1001,
lambd=0)
Y_predict, train_accuracy = nn_model_tf.predict(X, Y_onehot, parameters,
hidden_layer_dims,
layer_types)
print('Training accuracy: %f' % train_accuracy)
plot.show_decision_boundry(X, Y, Y_onehot, nn_model.predict, parameters,
hidden_layer_dims, layer_types)