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_with_dropout_adam():
X, Y, Y_onehot=input_data.loadRandomData()
layer_types=['relu','softmax',]
hidden_layer_dims=[120,]
parameters = nn_model.model_with_dropout_adam(X, Y_onehot, hidden_layer_dims, layer_types, learning_rate=0.5, num_iterations=2001)
Y_predict, train_accuracy = nn_model.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)
def check_dropout():
X, Y, Y_onehot = input_data.loadRandomData()
layer_types = [
'softmax',
]
layer_dims = [X.shape[0], Y_onehot.shape[0]]
parameters = nn_model.init_params(layer_dims)
gradient_check_with_dorpout(X,
Y,
layer_dims,
layer_types,
parameters,
num_params=2)
def check_softmax():
X, Y, Y_onehot = input_data.loadRandomData()
# X.shape=(2, 300), Y.shape=(1,300), Y_onehot=(3,300)
# number of examples = 300, number of classes = 3
layer_types = [
'softmax',
]
layer_dims = [X.shape[0], Y_onehot.shape[0]]
parameters = nn_model.init_params(layer_dims)
gradient_check(X,
Y_onehot,
layer_dims,
layer_types,
parameters,
epsilon=1e-7,
num_params=2,
lambd=1)
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)
