def tanhexp(xTrain, xTest, yTrain, yTest, hots):
print('EXPERIMENTO CON FUNCION DE ACTIVACION TANH')
# Primera red neuronal
nn = NeuralNetwork(7, [5, 4], 2, 3)
nn.set_activation([Tanh(), Tanh(), Tanh()])
nn.set_learning_rate([0.6, 0.6, 0.6])
train_plot_cm(nn, xTrain, xTest, yTrain, yTest,
epochs=60, encoding_dict=hots)
# Segunda red neuronal
nn = NeuralNetwork(7, [5, 4], 2, 3)
nn.set_activation([Tanh(), Tanh(), Tanh()])
nn.set_learning_rate([0.3, 0.3, 0.3])
train_plot_cm(nn, xTrain, xTest, yTrain, yTest,
epochs=60, encoding_dict=hots)
# Tercera red neuronal
nn = NeuralNetwork(7, [5, 4], 2, 3)
nn.set_activation([Tanh(), Tanh(), Tanh()])
nn.set_learning_rate([0.2, 0.2, 0.2])
train_plot_cm(nn, xTrain, xTest, yTrain, yTest,
epochs=60, encoding_dict=hots)
def test_set_activation(self):
self.assertEqual(Sigmoid().__class__,
self.nn.layers[0].get_activation().__class__)
self.assertEqual(Sigmoid().__class__,
self.nn.layers[1].get_activation().__class__)
self.assertEqual(Sigmoid().__class__,
self.nn.layers[2].get_activation().__class__)
self.nn.set_activation([Tanh(), Step(), Tanh()])
self.assertEqual(Tanh().__class__,
self.nn.layers[0].get_activation().__class__)
self.assertEqual(Step().__class__,
self.nn.layers[1].get_activation().__class__)
self.assertEqual(Tanh().__class__,
self.nn.layers[2].get_activation().__class__)
def twohl(xTrain, xTest, yTrain, yTest, hots):
print('EXPERIMENTO CON DOS CAPAS ESCONDIDAS')
# Primera red neuronal
nn = NeuralNetwork(7, [5, 4], 2, 3)
nn.set_activation([Sigmoid(), Sigmoid(), Sigmoid()])
nn.set_learning_rate([0.6, 0.6, 0.6])
train_plot_cm(nn, xTrain, xTest, yTrain, yTest,
epochs=50, encoding_dict=hots)
# Segunda red neuronal
nn = NeuralNetwork(7, [5, 4], 2, 3)
nn.set_activation([Tanh(), Tanh(), Tanh()])
nn.set_learning_rate([0.6, 0.6, 0.6])
train_plot_cm(nn, xTrain, xTest, yTrain, yTest,
epochs=50, encoding_dict=hots)
# Tercera red neuronal
nn = NeuralNetwork(7, [5, 4], 2, 3)
nn.set_activation([Sigmoid(), Sigmoid(), Sigmoid()])
nn.set_learning_rate([0.3, 0.3, 0.3])
train_plot_cm(nn, xTrain, xTest, yTrain, yTest,
epochs=50, encoding_dict=hots)
# Cuarta red neuronal
nn = NeuralNetwork(7, [10, 10], 2, 3)
nn.set_activation([Sigmoid(), Sigmoid(), Sigmoid()])
nn.set_learning_rate([0.6, 0.6, 0.6])
train_plot_cm(nn, xTrain, xTest, yTrain, yTest,
epochs=50, encoding_dict=hots)
# Quinta red neuronal
nn = NeuralNetwork(7, [1, 2], 2, 3)
nn.set_activation([Sigmoid(), Sigmoid(), Sigmoid()])
nn.set_learning_rate([0.6, 0.6, 0.6])
train_plot_cm(nn, xTrain, xTest, yTrain, yTest,
epochs=50, encoding_dict=hots)
def run_all_model(train_input,
train_target,
test_input,
test_target,
Sample_number,
save_plot=False):
# Define constants along the test
hidden_nb = 25
std = 0.1
eta = 3e-1
batch_size = 200
epochs_number = 1000
# Model 1. No dropout; constant learning rate (SGD)
print('\nModel 1: Optimizer: SGD; No dropout; ReLU; CrossEntropy')
# Define model name for plots
mname = 'Model1'
# Define structure of the network
linear_1 = Linear(2, hidden_nb)
relu_1 = Relu()
linear_2 = Linear(hidden_nb, hidden_nb)
relu_2 = Relu()
linear_3 = Linear(hidden_nb, hidden_nb)
relu_3 = Relu()
linear_4 = Linear(hidden_nb, 2)
loss = CrossEntropy()
model_1 = Sequential(linear_1,
relu_1,
linear_2,
relu_2,
linear_3,
relu_3,
linear_4,
loss=CrossEntropy())
# Initialize weights
model_1.normalize_parameters(mean=0, std=std)
# Define optimizer
optimizer = Sgd(eta)
# Train model
my_loss_1 = train_model(model_1, train_input, train_target, optimizer,
epochs_number, Sample_number, batch_size)
# Evalute model and produce plots
model_1_perf = evaluate_model(model_1,
train_input,
train_target,
test_input,
test_target,
my_loss_1,
save_plot,
mname=mname)
# Model 2. No dropout; decreasing learning rate (DecreaseSGD)
print('\nModel 2: Optimizer: DecreaseSGD; No dropout; ReLU; CrossEntropy')
# Define model name for plots
mname = 'Model2'
# Define structure of the network
linear_1 = Linear(2, hidden_nb)
relu_1 = Relu()
linear_2 = Linear(hidden_nb, hidden_nb)
relu_2 = Relu()
linear_3 = Linear(hidden_nb, hidden_nb)
relu_3 = Relu()
linear_4 = Linear(hidden_nb, 2)
model_2 = Sequential(linear_1,
relu_1,
linear_2,
relu_2,
linear_3,
relu_3,
linear_4,
loss=CrossEntropy())
# Initialize weights
model_2.normalize_parameters(mean=0, std=std)
# Define optimizer
optimizer = DecreaseSGD(eta)
# Train model
my_loss_2 = train_model(model_2, train_input, train_target, optimizer,
epochs_number, Sample_number, batch_size)
# Evalute model and produce plots
model_2_perf = evaluate_model(model_2,
train_input,
train_target,
test_input,
test_target,
my_loss_2,
save_plot,
mname=mname)
# Model 3. No dropout; Adam Optimizer
print('\nModel 3: Optimizer: Adam; No dropout; ReLU; CrossEntropy')
# Define model name for plots
mname = 'Model3'
# Custom hyperparameters
eta_adam = 1e-3
epochs_number_adam = 500
# Define structure of the network
linear_1 = Linear(2, hidden_nb)
relu_1 = Relu()
linear_2 = Linear(hidden_nb, hidden_nb)
relu_2 = Relu()
linear_3 = Linear(hidden_nb, hidden_nb)
relu_3 = Relu()
linear_4 = Linear(hidden_nb, 2)
loss = CrossEntropy()
model_3 = Sequential(linear_1,
relu_1,
linear_2,
relu_2,
linear_3,
relu_3,
linear_4,
loss=CrossEntropy())
# Initialize weights
model_3.normalize_parameters(mean=0, std=std)
# Define optimizer
optimizer = Adam(eta_adam, 0.9, 0.99, 1e-8)
# Train model
my_loss_3 = train_model(model_3, train_input, train_target, optimizer,
epochs_number_adam, Sample_number, batch_size)
# Evalute model and produce plots
model_3_perf = evaluate_model(model_3,
train_input,
train_target,
test_input,
test_target,
my_loss_3,
save_plot,
mname=mname)
# PLOT TO COMPARE OPTIMIZERS
if save_plot:
fig = plt.figure(figsize=(10, 4))
plt.plot(range(0, epochs_number), my_loss_1, linewidth=1)
plt.plot(range(0, epochs_number), my_loss_2, linewidth=1)
plt.plot(range(0, epochs_number_adam), my_loss_3, linewidth=1)
plt.legend(["SGD", "Decreasing SGD", "Adam"])
plt.title("Loss")
plt.xlabel("Epochs")
plt.savefig('output/compare_optimizers.pdf', bbox_inches='tight')
plt.close(fig)
# Model 4. Dropout; SGD
print('\nModel 4: Optimizer: SGD; Dropout; ReLU; CrossEntropy')
# Define model name for plots
mname = 'Model4'
# Define structure of the network
dropout = 0.15
linear_1 = Linear(2, hidden_nb)
relu_1 = Relu()
linear_2 = Linear(hidden_nb, hidden_nb, dropout=dropout)
relu_2 = Relu()
linear_3 = Linear(hidden_nb, hidden_nb, dropout=dropout)
relu_3 = Relu()
linear_4 = Linear(hidden_nb, 2)
model_4 = Sequential(linear_1,
relu_1,
linear_2,
relu_2,
linear_3,
relu_3,
linear_4,
loss=CrossEntropy())
# Initialize weights
model_4.normalize_parameters(mean=0, std=std)
# Define optimizer
optimizer = Sgd(eta)
# Train model
my_loss_4 = train_model(model_4, train_input, train_target, optimizer,
epochs_number, Sample_number, batch_size)
# Evalute model and produce plots
model_4_perf = evaluate_model(model_4,
train_input,
train_target,
test_input,
test_target,
my_loss_4,
save_plot,
mname=mname)
# PLOT TO COMPARE DROPOUT AND NO DROPOUT
if save_plot:
fig = plt.figure(figsize=(10, 4))
plt.plot(range(0, epochs_number), my_loss_1, linewidth=1)
plt.plot(range(0, epochs_number), my_loss_4, linewidth=1)
plt.legend(["Without Dropout", "With Dropout"])
plt.title("Loss")
plt.xlabel("Epochs")
plt.savefig('output/compare_dropout.pdf', bbox_inches='tight')
plt.close(fig)
print('\nEvaluation of different activation functions\n')
# Model 5. No Dropout; SGD; Tanh
print('\nModel 5: Optimizer: SGD; No dropout; Tanh; CrossEntropy')
# Define model name for plots
mname = 'Model5'
# Define structure of the network
linear_1 = Linear(2, hidden_nb)
relu_1 = Tanh()
linear_2 = Linear(hidden_nb, hidden_nb)
relu_2 = Tanh()
linear_3 = Linear(hidden_nb, hidden_nb)
relu_3 = Tanh()
linear_4 = Linear(hidden_nb, 2)
model_5 = Sequential(linear_1,
relu_1,
linear_2,
relu_2,
linear_3,
relu_3,
linear_4,
loss=CrossEntropy())
# Initialize weights
model_5.normalize_parameters(mean=0, std=std)
# Define optimizer
optimizer = Sgd(eta)
# Train model
my_loss_5 = train_model(model_5, train_input, train_target, optimizer,
epochs_number, Sample_number, batch_size)
# Evalute model and produce plots
model_5_perf = evaluate_model(model_5,
train_input,
train_target,
test_input,
test_target,
my_loss_5,
save_plot,
mname=mname)
# Model 6. Xavier Initialization
print(
'\nModel 6: Optimizer: SGD; No dropout; Tanh; Xavier initialization; CrossEntropy'
)
# Define model name for plots
mname = 'Model6'
# Define network structure
linear_1 = Linear(2, hidden_nb)
relu_1 = Tanh()
linear_2 = Linear(hidden_nb, hidden_nb)
relu_2 = Tanh()
linear_3 = Linear(hidden_nb, hidden_nb)
relu_3 = Tanh()
linear_4 = Linear(hidden_nb, 2)
model_6 = Sequential(linear_1,
relu_1,
linear_2,
relu_2,
linear_3,
relu_3,
linear_4,
loss=CrossEntropy())
model_6.xavier_parameters()
optimizer = Sgd()
# Train model
my_loss_6 = train_model(model_6, train_input, train_target, optimizer,
epochs_number, Sample_number, batch_size)
# Evalute model and produce plots
model_6_perf = evaluate_model(model_6,
train_input,
train_target,
test_input,
test_target,
my_loss_6,
save_plot,
mname=mname)
# Model 7. Sigmoid
print('\nModel 7: Optimizer: SGD; No dropout; Sigmoid; CrossEntropy')
# Define model name for plots
mname = 'Model7'
# Define parameter for sigmoid activation
p_lambda = 0.1
# Define network structure
linear_1 = Linear(2, hidden_nb)
relu_1 = Sigmoid(p_lambda)
linear_2 = Linear(hidden_nb, hidden_nb)
relu_2 = Sigmoid(p_lambda)
linear_3 = Linear(hidden_nb, hidden_nb)
relu_3 = Sigmoid(p_lambda)
linear_4 = Linear(hidden_nb, 2)
model_7 = Sequential(linear_1,
relu_1,
linear_2,
relu_2,
linear_3,
relu_3,
linear_4,
loss=CrossEntropy())
model_7.normalize_parameters(mean=0.5, std=1)
optimizer = Sgd(eta=0.5)
# Train model
my_loss_7 = train_model(model_7, train_input, train_target, optimizer,
epochs_number, Sample_number, batch_size)
# Evalute model and produce plots
model_7_perf = evaluate_model(model_7,
train_input,
train_target,
test_input,
test_target,
my_loss_7,
save_plot,
mname=mname)
# PLOT TO COMPARE EFFECT OF DIFFERENT ACTIVATIONS
if save_plot:
fig = plt.figure(figsize=(10, 4))
plt.plot(range(0, epochs_number), my_loss_1, linewidth=0.5)
plt.plot(range(0, epochs_number), my_loss_5, linewidth=0.5, alpha=0.8)
plt.plot(range(0, epochs_number), my_loss_6, linewidth=0.5, alpha=0.8)
plt.plot(range(0, epochs_number), my_loss_7, linewidth=0.5)
plt.legend(["Relu", "Tanh", "Tanh (Xavier)", "Sigmoid"])
plt.title("Loss")
plt.xlabel("Epochs")
plt.savefig('output/compare_activations.pdf', bbox_inches='tight')
plt.close(fig)
print('\nEvaluation of base model with MSE loss\n')
# Model 8. MSE loss
print('\nModel 8: Optimizer: SGD; No dropout; Relu; MSE')
# Define model name for plots
mname = 'Model8'
linear_1 = Linear(2, hidden_nb)
relu_1 = Relu()
linear_2 = Linear(hidden_nb, hidden_nb)
relu_2 = Relu()
linear_3 = Linear(hidden_nb, hidden_nb)
relu_3 = Relu()
linear_4 = Linear(hidden_nb, 2)
loss = LossMSE()
model_8 = Sequential(linear_1,
relu_1,
linear_2,
relu_2,
linear_3,
relu_3,
linear_4,
loss=loss)
model_8.normalize_parameters(mean=0, std=std)
optimizer = Sgd(eta)
# Train model
my_loss_8 = train_model(model_8, train_input, train_target, optimizer,
epochs_number, Sample_number, batch_size)
# Evalute model and produce plots
model_8_perf = evaluate_model(model_8,
train_input,
train_target,
test_input,
test_target,
my_loss_8,
save_plot,
mname=mname)
print('Evaluation done! ')
train_loss = torch.tensor([
model_1_perf[0], model_2_perf[0], model_3_perf[0], model_4_perf[0],
model_5_perf[0], model_6_perf[0], model_7_perf[0], model_8_perf[0]
])
train_error = torch.tensor([
model_1_perf[1], model_2_perf[1], model_3_perf[1], model_4_perf[1],
model_5_perf[1], model_6_perf[1], model_7_perf[1], model_8_perf[1]
])
test_loss = torch.tensor([
model_1_perf[2], model_2_perf[2], model_3_perf[2], model_4_perf[2],
model_5_perf[2], model_6_perf[2], model_7_perf[2], model_8_perf[2]
])
test_error = torch.tensor([
model_1_perf[3], model_2_perf[3], model_3_perf[3], model_4_perf[3],
model_5_perf[3], model_6_perf[3], model_7_perf[3], model_8_perf[3]
])
return train_loss, train_error, test_loss, test_error
from Activation import Tanh
from Gates import AddGate, MultiplyGate
mulGate = MultiplyGate()
addGate = AddGate()
activation = Tanh()
class Layer:
def forward(self, x, prev, U, W, V):
self.mulu = mulGate.forward(U, x)
self.mulw = mulGate.forward(W, prev)
self.add = addGate.forward(self.mulw, self.mulu)
self.s = activation.forward(self.add)
self.mulv = mulGate.forward(V, self.s)
def backward(self, x, prev, U, W, V, diff, dmulv):
self.forward(x, prev, U, W, V)
dV, dsv = mulGate.backward(V, self.s, dmulv)
ds = dsv + diff
dadd = activation.backward(self.add, ds)
dmulw, dmulu = addGate.backward(self.mulw, self.mulu, dadd)
dW, dprev = mulGate.backward(W, prev, dmulw)
dU, dx = mulGate.backward(U, x, dmulu)
return (dprev, dU, dW, dV)
###############################################################
if args.load:
train_conv = False
train_fc = True
weights_conv = args.load
weights_fc = args.load
else:
train_conv = False
train_fc = True
weights_conv = '../vgg_weights/vgg_weights.npy'
weights_fc = None
if args.act == 'tanh':
act = Tanh()
elif args.act == 'relu':
act = Relu()
else:
assert (False)
###############################################################
dropout_rate = tf.placeholder(tf.float32, shape=())
learning_rate = tf.placeholder(tf.float32, shape=())
l0 = Convolution(input_sizes=[batch_size, 224, 224, 3],
filter_sizes=[3, 3, 3, 64],
num_classes=num_classes,
init_filters=args.init,
strides=[1, 1, 1, 1],
def test_set_activation(self):
self.assertEqual(Sigmoid().__class__,
self.layer1.get_activation().__class__)
self.layer1.set_activation(Tanh())
self.assertEqual(Tanh().__class__,
self.layer1.get_activation().__class__)
############################################## tf.set_random_seed(0) tf.reset_default_graph() batch_size = tf.placeholder(tf.int32, shape=()) XTRAIN = tf.placeholder(tf.float32, [None, 32, 32, 3]) YTRAIN = tf.placeholder(tf.float32, [None, 100]) XTRAIN = tf.map_fn(lambda frame: tf.image.per_image_standardization(frame), XTRAIN) XTEST = tf.placeholder(tf.float32, [None, 32, 32, 3]) YTEST = tf.placeholder(tf.float32, [None, 100]) XTEST = tf.map_fn(lambda frame1: tf.image.per_image_standardization(frame1), XTEST) l0 = Convolution(input_sizes=[batch_size, 32, 32, 3], filter_sizes=[5, 5, 3, 96], num_classes=100, init_filters=args.init, strides=[1, 1, 1, 1], padding="SAME", alpha=ALPHA, activation=Tanh(), last_layer=False) l1 = MaxPool(size=[batch_size, 32, 32, 96], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="SAME") l2 = FeedbackConv(size=[batch_size, 16, 16, 96], num_classes=100, sparse=sparse, rank=rank) l3 = Convolution(input_sizes=[batch_size, 16, 16, 96], filter_sizes=[5, 5, 96, 128], num_classes=100, init_filters=args.init, strides=[1, 1, 1, 1], padding="SAME", alpha=ALPHA, activation=Tanh(), last_layer=False) l4 = MaxPool(size=[batch_size, 16, 16, 128], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="SAME") l5 = FeedbackConv(size=[batch_size, 8, 8, 128], num_classes=100, sparse=sparse, rank=rank) l6 = Convolution(input_sizes=[batch_size, 8, 8, 128], filter_sizes=[5, 5, 128, 256], num_classes=100, init_filters=args.init, strides=[1, 1, 1, 1], padding="SAME", alpha=ALPHA, activation=Tanh(), last_layer=False) l7 = MaxPool(size=[batch_size, 8, 8, 256], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="SAME") l8 = FeedbackConv(size=[batch_size, 4, 4, 256], num_classes=100, sparse=sparse, rank=rank) l9 = ConvToFullyConnected(shape=[4, 4, 256]) l10 = FullyConnected(size=[4*4*256, 2048], num_classes=100, init_weights=args.init, alpha=ALPHA, activation=Tanh(), last_layer=False) l11 = FeedbackFC(size=[4*4*256, 2048], num_classes=100, sparse=sparse, rank=rank)
get_conv1_bias = tf.get_default_graph().get_tensor_by_name(os.path.split(conv1.name)[0] + '/bias:0')
get_conv2_weights = tf.get_default_graph().get_tensor_by_name(os.path.split(conv2.name)[0] + '/kernel:0')
get_conv2_bias = tf.get_default_graph().get_tensor_by_name(os.path.split(conv2.name)[0] + '/bias:0')
get_conv3_weights = tf.get_default_graph().get_tensor_by_name(os.path.split(conv3.name)[0] + '/kernel:0')
get_conv3_bias = tf.get_default_graph().get_tensor_by_name(os.path.split(conv3.name)[0] + '/bias:0')
get_conv4_weights = tf.get_default_graph().get_tensor_by_name(os.path.split(conv4.name)[0] + '/kernel:0')
get_conv4_bias = tf.get_default_graph().get_tensor_by_name(os.path.split(conv4.name)[0] + '/bias:0')
get_conv5_weights = tf.get_default_graph().get_tensor_by_name(os.path.split(conv5.name)[0] + '/kernel:0')
get_conv5_bias = tf.get_default_graph().get_tensor_by_name(os.path.split(conv5.name)[0] + '/bias:0')
else:
l0 = Convolution(input_sizes=[batch_size, 227, 227, 3], filter_sizes=[11, 11, 3, 96], num_classes=num_classes, init_filters=args.init, strides=[1, 4, 4, 1], padding="VALID", alpha=ALPHA, activation=Tanh(), bias=0.0, last_layer=False)
l1 = MaxPool(size=[batch_size, 55, 55, 96], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID")
l2 = Convolution(input_sizes=[batch_size, 27, 27, 96], filter_sizes=[5, 5, 96, 256], num_classes=num_classes, init_filters=args.init, strides=[1, 1, 1, 1], padding="SAME", alpha=ALPHA, activation=Tanh(), bias=0.0, last_layer=False)
l3 = MaxPool(size=[batch_size, 27, 27, 256], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID")
l4 = Convolution(input_sizes=[batch_size, 13, 13, 256], filter_sizes=[3, 3, 256, 384], num_classes=num_classes, init_filters=args.init, strides=[1, 1, 1, 1], padding="SAME", alpha=ALPHA, activation=Tanh(), bias=0.0, last_layer=False)
l5 = Convolution(input_sizes=[batch_size, 13, 13, 384], filter_sizes=[3, 3, 384, 384], num_classes=num_classes, init_filters=args.init, strides=[1, 1, 1, 1], padding="SAME", alpha=ALPHA, activation=Tanh(), bias=0.0, last_layer=False)
l6 = Convolution(input_sizes=[batch_size, 13, 13, 384], filter_sizes=[3, 3, 384, 256], num_classes=num_classes, init_filters=args.init, strides=[1, 1, 1, 1], padding="SAME", alpha=ALPHA, activation=Tanh(), bias=0.0, last_layer=False)
l7 = MaxPool(size=[batch_size, 13, 13, 256], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID")
l8 = ConvToFullyConnected(shape=[6, 6, 256])
l9 = FullyConnected(size=[6*6*256, 4096], num_classes=num_classes, init_weights=args.init, alpha=ALPHA, activation=Tanh(), bias=0.0, last_layer=False)
