def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
x_train, y_train, x_valid, y_valid, x_test, y_test = mnist("./")
lr = id2config[incumbent]['config']["learning_rate"]
num_filters = int(id2config[incumbent]['config']["num_filters"])
batch_size = int(id2config[incumbent]['config']["batch_size"])
filter_size = int(id2config[incumbent]['config']["filter_size"])
epochs=6
print(lr)
print(num_filters)
print(batch_size)
print(filter_size)
x_image = tf.placeholder(tf.float32, shape=[None,28,28,1], name='x')
y_ = tf.placeholder(tf.float32, shape=[None, 10], name='y_')
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.x_train, self.y_train, self.x_valid, self.y_valid, self.x_test, self.y_test = mnist("./")
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.x_train, self.y_train, self.x_valid, self.y_valid, self.x_test, self.y_test = mnist(
"../exercise1/data")
# Plots the performance of the best found validation error over time
all_runs = res.get_all_runs()
print('All runs:')
print(all_runs)
# Let's plot the observed losses grouped by budget,
import hpbandster.visualization as hpvis
hpvis.losses_over_time(all_runs)
import matplotlib.pyplot as plt
plt.savefig("random_search.png")
# TODO: retrain the best configuration (called incumbent) and compute the test error
# get the best hyperparameters
learning_rate = id2config[incumbent]['config']['learning_rate']
batch_size = id2config[incumbent]['config']['batch_size']
filter_size = id2config[incumbent]['config']['filter_size']
num_filters = id2config[incumbent]['config']['num_filters']
# load data
x_train, y_train, x_valid, y_valid, x_test, y_test = mnist('../exercise1/data')
# train the best model agian
learning_curve, model, _ = train_and_validate(x_train, y_train, x_valid,
y_valid, 12, learning_rate,
num_filters, batch_size,
filter_size)
test_err = test(x_test, y_test, model)
print(test_err)
def plot_learning_curves(lcurves, curve_labels, title):
fig, ax = plt.subplots()
ax.set(title=title, xlabel='Epoch', ylabel='Accuracy')
plots = ax.plot(lcurves)
ax.legend(plots, curve_labels, loc=4)
fig.savefig(title + '.pdf', format='pdf')
#plt.show()
if __name__ == "__main__":
# hyperparameters
lr = 1e-3
num_filters = 32
batch_size = 128
epochs = 12
# train and test datasets
x_train, y_train, x_valid, y_valid, x_test, y_test = cnn.mnist()
# trying different learning rates
learning_rates = [0.1, 0.01, 0.001, 0.0001]
kernel_sizes = [3]
lcs = try_hyperparameters(learning_rates, kernel_sizes)
plot_learning_curves(lcs, learning_rates, 'Learning rates')
# trying different filter sizes
learning_rates = [0.001]
kernel_sizes = [1, 3, 5, 7]
lcs = try_hyperparameters(learning_rates, kernel_sizes)
plot_learning_curves(lcs, kernel_sizes, 'kernel sizes')
plt.title(legend['title'], size=22)
plt.xlabel('Epoch', size=18)
plt.ylabel('Validation error', size=18)
# plt.xlim(0,len(learning_curves_lst[0]))
for i in range(len(learning_curves_lst)):
plt.plot(learning_curves_lst[i], label=legend['graph_labels'][i])
plt.legend(fontsize=16)
plt.savefig('%s.pdf' % legend['title'])
if __name__ == "__main__":
# get data
train_x, train_y, val_x, val_y, test_x, test_y = mnist('../exercise1/data')
# 2: test the effect of the learning rate
learning_rates = [0.1, 0.01, 0.001, 0.0001]
learning_curves_lr = []
for lr in learning_rates:
learning_curve, model, _ = train_and_validate(train_x,
train_y,
val_x,
val_y,
num_epochs=NUM_EPOCHS,
lr=lr,
num_filters=16,
kernel_size=3,
batch_size=64)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# <JAB>
self.x_train, self.y_train, self.x_valid, self.y_valid, self.x_test, self.y_test = mnist(
"./")
#(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
#self.x_train = train_images[:50000]
#self.y_train = train_labels[:50000]
#self.x_valid = train_images[50000:]
#self.y_valid = train_labels[50000:]
#self.x_test = test_images
#self.y_test = test_labels
#
#
print('Training Data Shapes (x, y): (', self.x_train.shape, ', ',
self.y_train.shape, ')')
print('Validation Data Shapes (x, y): (', self.x_valid.shape, ', ',
self.y_valid.shape, ')')
print('Test Data Shapes (x, y): (', self.x_test.shape, ', ',
self.y_test.shape, ')')