def main(FLAGS):
(x_train, y_train, x_test, y_test) = mnist_util.load_mnist_data()
x = Input(shape=(
28,
28,
1,
), name="input")
y = model.cryptonets_model(x)
cryptonets_model = Model(inputs=x, outputs=y)
print(cryptonets_model.summary())
def loss(labels, logits):
return keras.losses.categorical_crossentropy(labels,
logits,
from_logits=True)
optimizer = SGD(learning_rate=0.008, momentum=0.9)
cryptonets_model.compile(optimizer=optimizer,
loss=loss,
metrics=["accuracy"])
cryptonets_model.fit(x_train,
y_train,
epochs=FLAGS.epochs,
batch_size=FLAGS.batch_size,
validation_data=(x_test, y_test),
verbose=1)
test_loss, test_acc = cryptonets_model.evaluate(x_test, y_test, verbose=1)
print("Test accuracy:", test_acc)
# Squash weights and save model
weights = squash_layers(cryptonets_model,
tf.compat.v1.keras.backend.get_session())
(conv1_weights, squashed_weights, fc1_weights, fc_1half_weights,
fc2_weights) = weights[0:5]
tf.reset_default_graph()
sess = tf.compat.v1.Session()
x = Input(shape=(
28,
28,
1,
), name="input")
y = model.cryptonets_model_squashed(x, conv1_weights, squashed_weights,
fc_1half_weights, fc2_weights)
sess.run(tf.compat.v1.global_variables_initializer())
mnist_util.save_model(
sess,
["output/BiasAdd"],
"./models",
"testnets_1",
)
def train_model():
(x_train, y_train, x_test, y_test) = load_mnist_data()
x = Input(
shape=(
28,
28,
1,
), name="input")
y = mnist_mlp_model(x)
mlp_model = Model(inputs=x, outputs=y)
print(mlp_model.summary())
def loss(labels, logits):
return categorical_crossentropy(labels, logits, from_logits=True)
optimizer = Adam()
mlp_model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
t0 = time.perf_counter()
mlp_model.fit(
x_train,
y_train,
epochs=10,
batch_size=128,
validation_data=(x_test, y_test),
verbose=1)
t1 = time.perf_counter()
cur_times['t_training'] = delta_ms(t0, t1)
test_loss, test_acc = mlp_model.evaluate(x_test, y_test, verbose=1)
print("\nTest accuracy:", test_acc)
save_model(
tf.compat.v1.keras.backend.get_session(),
["output/BiasAdd"],
"./models",
"mlp",
)
# If we want to have training and testing in the same run, we must clean up after training
tf.keras.backend.clear_session
tf.reset_default_graph()
def main(FLAGS):
(x_train, y_train, x_test, y_test) = mnist_util.load_mnist_data()
x = Input(
shape=(
28,
28,
1,
), name="input")
y = model.mnist_mlp_model(x)
mlp_model = Model(inputs=x, outputs=y)
print(mlp_model.summary())
def loss(labels, logits):
return categorical_crossentropy(labels, logits, from_logits=True)
optimizer = SGD(learning_rate=0.008, momentum=0.9)
mlp_model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
mlp_model.fit(
x_train,
y_train,
epochs=FLAGS.epochs,
batch_size=FLAGS.batch_size,
validation_data=(x_test, y_test),
verbose=1)
test_loss, test_acc = mlp_model.evaluate(x_test, y_test, verbose=1)
print("\nTest accuracy:", test_acc)
mnist_util.save_model(
tf.compat.v1.keras.backend.get_session(),
["output/BiasAdd"],
"./models",
"mlp",
)
def train_model():
(x_train, y_train, x_test, y_test) = load_mnist_data()
x = Input(shape=(
28,
28,
1,
), name="input")
y = cryptonets_model(x)
cryptonets_model_var = Model(inputs=x, outputs=y)
print(cryptonets_model_var.summary())
def loss(labels, logits):
return categorical_crossentropy(labels, logits, from_logits=True)
optimizer = Adam()
cryptonets_model_var.compile(optimizer=optimizer,
loss=loss,
metrics=["accuracy"])
t0 = time.perf_counter()
cryptonets_model_var.fit(x_train,
y_train,
epochs=10,
batch_size=128,
validation_data=(x_test, y_test),
verbose=1)
t1 = time.perf_counter()
cur_times['t_training'] = delta_ms(t0, t1)
test_loss, test_acc = cryptonets_model_var.evaluate(x_test,
y_test,
verbose=1)
print("Test accuracy:", test_acc)
# Squash weights and save model
weights = squash_layers(cryptonets_model_var,
tf.compat.v1.keras.backend.get_session())
(conv1_weights, squashed_weights, fc1_weights, fc2_weights, act1_weights,
act2_weights) = weights[0:6]
tf.reset_default_graph()
sess = tf.compat.v1.Session()
x = Input(shape=(
28,
28,
1,
), name="input")
y = cryptonets_model_squashed(x, conv1_weights, squashed_weights,
fc2_weights, act1_weights, act2_weights)
sess.run(tf.compat.v1.global_variables_initializer())
save_model(
sess,
["output/BiasAdd"],
"./models",
"cryptonets",
)
# If we want to have training and testing in the same run, we must clean up after training
tf.keras.backend.clear_session
tf.reset_default_graph()
def main(FLAGS):
y_train_logit_file = 'old_logit_out_train.h5' # 'acc_train_logit_out.h5' #
y_test_logit_file = 'old_logit_out_test.h5' #'acc_test_logit_out.h5' #
logit_scale = FLAGS.logit_scale #[0.1, 1, 10, 100]
(x_train, y_train, y_train_label, x_test, y_test,
y_test_label) = mnist_util.load_mnist_logit_data(
y_train_logit_file=y_train_logit_file,
y_test_logit_file=y_test_logit_file,
logit_scale=logit_scale)
#generate valid architectures for a given security level and fixed layer level:
#architectures = generate_architecture(4096) TODO
max_levels = 4
input_size = 784
output_size = 10
#architectures = generate_architectures(max_levels, input_size, output_size, include_bottleneck=True)
experiments = [50, 100, 200, 400, 800]
for exp in experiments:
architectures = [[("dense", exp), ("activation", "square"),
("dense", 10)]]
#[]
# [("dense", 100), ("dense", 800), ("activation", "square"), ("dense", 10)],
# [("dense", 800), ("dense", 800), ("activation", "square"), ("dense", 10)],
# [("dense", 800), ("dense", 800), ("dense", 10)]
# [("dense", 800), ("activation", "square"), ("dense", 10)],
# [("dense", 800), ("dense", 10), ("activation", "square")]]
layer_list = architectures[0]
x = Input(shape=(
28,
28,
1,
), name="input")
y = cryptonets_model_no_conv(x, layer_list)
cryptonets_model = Model(inputs=x, outputs=y)
print(cryptonets_model.summary())
optimizer = SGD(learning_rate=0.008, momentum=0.9)
cryptonets_model.compile(optimizer=optimizer,
loss='mean_squared_error',
metrics=[logit_accuracy])
#cryptonets_model.compile(
# optimizer=optimizer, loss=loss, metrics=["accuracy"])
cryptonets_model.fit(x_train,
y_train,
epochs=FLAGS.epochs,
batch_size=FLAGS.batch_size,
validation_data=(x_test, y_test_label),
verbose=1)
test_loss, test_acc = cryptonets_model.evaluate(
x_test, y_test_label, verbose=1
) #should this be y-test? No, evaluating against y_Test_label
print("Test accuracy:", test_acc)
# Squash weights and save model
weights, compressed_layer_list = squash_layers_variable(
cryptonets_model, tf.compat.v1.keras.backend.get_session(),
layer_list)
tf.reset_default_graph()
sess = tf.compat.v1.Session()
x = Input(shape=(
28,
28,
1,
), name="input")
y = cryptonets_model_no_conv_squashed(x, weights,
compressed_layer_list)
sess.run(tf.compat.v1.global_variables_initializer())
save_name = FLAGS.save_file + str(exp)
mnist_util.save_model(
sess,
["output/BiasAdd"],
"./models",
save_name,
)
tf.reset_default_graph()
sess = tf.compat.v1.Session()
def main(FLAGS):
logit_scale = FLAGS.logit_scale #[0.1, 1, 10, 100]
(x_train, y_train, y_train_label, x_test, y_test,
y_test_label) = mnist_util.load_mnist_logit_data_acc(
logit_scale=logit_scale)
#generate valid architectures for a given security level and fixed layer level:
#architectures = generate_architecture(4096) TODO
max_levels = 4
input_size = 784
output_size = 10
print("TESTING A LOT OF SHIT")
print(x_train.shape, y_train.shape, y_train_label.shape, x_test.shape,
y_test.shape, y_test_label.shape)
print("log1", y_train[0])
print("label", y_train_label[0])
print(np.argmax(y_test[0:5], axis=1), np.argmax(y_test_label[0:5], axis=1))
#architectures = generate_architectures(max_levels, input_size, output_size, include_bottleneck=True)
architectures = [[("dense", 100), ("activation", "square"), ("dense", 10)]]
#[]
# [("dense", 100), ("dense", 800), ("activation", "square"), ("dense", 10)],
# [("dense", 800), ("dense", 800), ("activation", "square"), ("dense", 10)],
# [("dense", 800), ("dense", 800), ("dense", 10)]
# [("dense", 800), ("activation", "square"), ("dense", 10)],
# [("dense", 800), ("dense", 10), ("activation", "square")]]
print("Architectures to test:")
for i in range(len(architectures)):
print(f"### ARCHITECTURE {i} ### ")
print(architectures[i])
accuracies = []
for layer_list in architectures:
x = Input(shape=(
28,
28,
1,
), name="input")
y = cryptonets_model_no_conv(x, layer_list)
cryptonets_model = Model(inputs=x, outputs=y)
print(cryptonets_model.summary())
optimizer = SGD(learning_rate=0.008, momentum=0.9)
#optimizer = Adam(learning_rate=0.001)
cryptonets_model.compile(optimizer=optimizer,
loss='mean_squared_error',
metrics=[logit_accuracy])
#cryptonets_model.compile(
# optimizer=optimizer, loss=loss, metrics=["accuracy"])
cryptonets_model.fit(
x_train,
y_train, #y_train_label
epochs=FLAGS.epochs,
batch_size=FLAGS.batch_size,
validation_data=(x_test, y_test_label), #y_test??
verbose=1)
test_loss, test_acc = cryptonets_model.evaluate(
x_test, y_test_label, verbose=1
) #should this be y-test? No, evaluating against y_Test_label
print("Test accuracy:", test_acc)
accuracies.append(test_acc)
#reset graph
tf.reset_default_graph()
sess = tf.compat.v1.Session()
best_model = np.argmax(accuracies)
layer_list = architectures[best_model]
print("architecture, test_acc")
for i in range(len(architectures)):
print(architectures[i][0:2], accuracies[i])
print("best model", best_model, layer_list, accuracies[best_model])
x = Input(shape=(
28,
28,
1,
), name="input")
y = cryptonets_model_no_conv(x, layer_list)
cryptonets_model = Model(inputs=x, outputs=y)
print(cryptonets_model.summary())
optimizer = SGD(learning_rate=0.008, momentum=0.9)
cryptonets_model.compile(optimizer=optimizer,
loss='mean_squared_error',
metrics=[logit_accuracy])
#cryptonets_model.compile(
# optimizer=optimizer, loss=loss, metrics=["accuracy"])
cryptonets_model.fit(x_train,
y_train,
epochs=FLAGS.epochs,
batch_size=FLAGS.batch_size,
validation_data=(x_test, y_test_label),
verbose=1)
test_loss, test_acc = cryptonets_model.evaluate(
x_test, y_test_label,
verbose=1) #should this be y-test? No, evaluating against y_Test_label
print("Test accuracy:", test_acc)
# Squash weights and save model
weights, compressed_layer_list = squash_layers_variable(
cryptonets_model, tf.compat.v1.keras.backend.get_session(), layer_list)
tf.reset_default_graph()
sess = tf.compat.v1.Session()
x = Input(shape=(
28,
28,
1,
), name="input")
y = cryptonets_model_no_conv_squashed(x, weights, compressed_layer_list)
sess.run(tf.compat.v1.global_variables_initializer())
mnist_util.save_model(
sess,
["output/BiasAdd"],
"./models",
FLAGS.save_file,
)