def create_student_model(teacher_model, num_classes, x_train, y_train):
dense1 = teacher_model.get_layer(0)
d1_kernel, d1_bias = dense1.get_weights(teacher_model.ffmodel)
print(d1_kernel.shape, d1_bias.shape)
# print(d1_kernel)
# print(d1_bias)
dense2 = teacher_model.get_layer(1)
d2_kernel, d2_bias = dense2.get_weights(teacher_model.ffmodel)
dense3 = teacher_model.get_layer(2)
d3_kernel, d3_bias = dense3.get_weights(teacher_model.ffmodel)
model = Sequential()
model.add(Dense(512, input_shape=(784,), activation="relu"))
model.add(Dense(512, activation="relu"))
model.add(Dense(num_classes))
model.add(Activation("softmax"))
opt = flexflow.keras.optimizers.SGD(learning_rate=0.01)
model.compile(optimizer=opt)
dense1s = model.get_layer(0)
dense2s = model.get_layer(1)
dense3s = model.get_layer(2)
dense1s.set_weights(model.ffmodel, d1_kernel, d1_bias)
dense2s.set_weights(model.ffmodel, d2_kernel, d2_bias)
dense3s.set_weights(model.ffmodel, d3_kernel, d3_bias)
d3_kernel, d3_bias = dense3s.get_weights(model.ffmodel)
print(d3_kernel)
print(d3_bias)
model.fit(x_train, y_train, epochs=1)
def create_student_model_cnn(teacher_model, num_classes, x_train, y_train):
conv1 = teacher_model.get_layer(0)
c1_kernel, c1_bias = conv1.get_weights(teacher_model.ffmodel)
print(c1_kernel.shape, c1_bias.shape)
conv2 = teacher_model.get_layer(1)
c2_kernel, c2_bias = conv2.get_weights(teacher_model.ffmodel)
dense1 = teacher_model.get_layer(4)
d1_kernel, d1_bias = dense1.get_weights(teacher_model.ffmodel)
dense2 = teacher_model.get_layer(5)
d2_kernel, d2_bias = dense2.get_weights(teacher_model.ffmodel)
model = Sequential()
model.add(Conv2D(filters=32, input_shape=(1,28,28), kernel_size=(3,3), strides=(1,1), padding=(1,1), activation="relu"))
model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding=(1,1), activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding="valid"))
model.add(Flatten())
model.add(Dense(128, activation="relu"))
model.add(Dense(num_classes))
model.add(Activation("softmax"))
opt = flexflow.keras.optimizers.SGD(learning_rate=0.01)
model.compile(optimizer=opt)
conv1s = model.get_layer(0)
conv2s = model.get_layer(1)
dense1s = model.get_layer(4)
dense2s = model.get_layer(5)
conv1s.set_weights(model.ffmodel, c1_kernel, c1_bias)
conv2s.set_weights(model.ffmodel, c2_kernel, c2_bias)
dense1s.set_weights(model.ffmodel, d1_kernel, d1_bias)
dense2s.set_weights(model.ffmodel, d2_kernel, d2_bias)
print(model.summary())
model.fit(x_train, y_train, epochs=1)
def create_student_model_mlp(teacher_model, num_classes, x_train, y_train):
dense1 = teacher_model.get_layer(index=0)
d1_kernel, d1_bias = dense1.get_weights(teacher_model.ffmodel)
print(d1_kernel.shape, d1_bias.shape)
dense2 = teacher_model.get_layer(index=1)
d2_kernel, d2_bias = dense2.get_weights(teacher_model.ffmodel)
dense3 = teacher_model.get_layer(index=2)
d3_kernel, d3_bias = dense3.get_weights(teacher_model.ffmodel)
model = Sequential()
model.add(Dense(512, input_shape=(784, ), activation="relu"))
model.add(Dense(512, activation="relu"))
model.add(Dense(num_classes))
model.add(Activation("softmax"))
opt = flexflow.keras.optimizers.SGD(learning_rate=0.01)
model.compile(optimizer=opt,
loss='sparse_categorical_crossentropy',
metrics=['accuracy', 'sparse_categorical_crossentropy'])
dense1s = model.get_layer(index=0)
dense2s = model.get_layer(index=1)
dense3s = model.get_layer(index=2)
dense1s.set_weights(model.ffmodel, d1_kernel, d1_bias)
dense2s.set_weights(model.ffmodel, d2_kernel, d2_bias)
dense3s.set_weights(model.ffmodel, d3_kernel, d3_bias)
d3_kernel, d3_bias = dense3s.get_weights(model.ffmodel)
print(d3_kernel)
print(d3_bias)
model.fit(x_train,
y_train,
epochs=5,
callbacks=[
VerifyMetrics(ModelAccuracy.MNIST_MLP),
EpochVerifyMetrics(ModelAccuracy.MNIST_MLP)
])
def create_student_model_cnn(teacher_model, num_classes, x_train, y_train):
conv1 = teacher_model.get_layer(index=0)
c1_kernel, c1_bias = conv1.get_weights(teacher_model.ffmodel)
print(c1_kernel.shape, c1_bias.shape)
conv2 = teacher_model.get_layer(index=1)
c2_kernel, c2_bias = conv2.get_weights(teacher_model.ffmodel)
dense1 = teacher_model.get_layer(index=4)
d1_kernel, d1_bias = dense1.get_weights(teacher_model.ffmodel)
dense2 = teacher_model.get_layer(index=5)
d2_kernel, d2_bias = dense2.get_weights(teacher_model.ffmodel)
model = Sequential()
model.add(Conv2D(filters=32, input_shape=(1,28,28), kernel_size=(3,3), strides=(1,1), padding=(1,1), activation="relu"))
model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding=(1,1), activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding="valid"))
model.add(Flatten())
model.add(Dense(128, activation="relu", name="dense1"))
model.add(Dense(num_classes))
model.add(Activation("softmax"))
opt = flexflow.keras.optimizers.SGD(learning_rate=0.01)
model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy', 'sparse_categorical_crossentropy'])
conv1s = model.get_layer(index=0)
conv2s = model.get_layer(index=1)
dense1s = model.get_layer(name="dense1")
dense2s = model.get_layer(index=5)
conv1s.set_weights(model.ffmodel, c1_kernel, c1_bias)
conv2s.set_weights(model.ffmodel, c2_kernel, c2_bias)
dense1s.set_weights(model.ffmodel, d1_kernel, d1_bias)
dense2s.set_weights(model.ffmodel, d2_kernel, d2_bias)
print(model.summary())
model.fit(x_train, y_train, epochs=5, callbacks=[VerifyMetrics(ModelAccuracy.MNIST_CNN), EpochVerifyMetrics(ModelAccuracy.MNIST_CNN)])
def create_teacher_model(num_classes, x_train, y_train):
model = Sequential()
model.add(Dense(512, input_shape=(784,), activation="relu"))
model.add(Dense(512, activation="relu"))
model.add(Dense(num_classes))
model.add(Activation("softmax"))
opt = flexflow.keras.optimizers.SGD(learning_rate=0.01)
model.compile(optimizer=opt)
model.fit(x_train, y_train, epochs=1)
dense3 = model.get_layer(2)
d3_kernel, d3_bias = dense3.get_weights(model.ffmodel)
print(d3_bias)
d3_kernel = np.reshape(d3_kernel, (d3_kernel.shape[1], d3_kernel.shape[0]))
print(d3_kernel)
return model
def create_teacher_model_mlp(num_classes, x_train, y_train):
model = Sequential()
model.add(Dense(512, input_shape=(784, ), activation="relu"))
model.add(Dense(512, activation="relu"))
model.add(Dense(num_classes))
model.add(Activation("softmax"))
opt = flexflow.keras.optimizers.SGD(learning_rate=0.01)
model.compile(optimizer=opt,
loss='sparse_categorical_crossentropy',
metrics=['accuracy', 'sparse_categorical_crossentropy'])
model.fit(x_train, y_train, epochs=1)
dense3 = model.get_layer(index=2)
d3_kernel, d3_bias = dense3.get_weights(model.ffmodel)
print(d3_bias)
d3_kernel = np.reshape(d3_kernel, (d3_kernel.shape[1], d3_kernel.shape[0]))
print(d3_kernel)
return model