def __init__(self,
in_dim,
hl_dims,
bias=False,
activation_fun=tf.nn.relu,
batch_normalization=True):
self.in_dim = in_dim
self.hl_dims = hl_dims
self.bias = bias
self.activation_func = activation_fun
self.batch_normalization = batch_normalization
self.fc_layers = []
# This must be equal, in order to add residual
assert in_dim == hl_dims[-1]
idim = self.in_dim
for i in range(len(hl_dims)):
if i == 0: # Give Relu NN activation function
self.fc_layers.append(
FullyConnectedLayer(idim,
hl_dims[i],
bias=self.bias,
activation_fun=activation_fun))
else: # Give No activation function
self.fc_layers.append(
FullyConnectedLayer(idim,
hl_dims[i],
bias=self.bias,
activation_fun=None))
idim = hl_dims[i]
def _build_architecture_get_prediction_and_regularization_cost(
architecture, weight_decay, current_input):
architecture_built = list()
regularization_cost = Variable(0.0)
weight_decay_variable = Variable(weight_decay) # TODO: constant
previous_layer_output = architecture[0]['input']
for layer_dictionary in architecture:
assert previous_layer_output == layer_dictionary["input"], \
'Inconsistent architecture: can not feed {} outputs to {} inputs'.format(
previous_layer_output,
layer_dictionary['input']
)
activation_function = activation_function_name_to_class[
layer_dictionary["nonlinear"]]
regularization_method = regularization_method_name_to_class[
layer_dictionary["regularization"]]
layer = FullyConnectedLayer(layer_dictionary["input"],
layer_dictionary["output"],
activation_function, current_input)
regularization_cost = Add(
regularization_cost,
Multiply(weight_decay_variable,
regularization_method(layer.get_weight())))
architecture_built.append(layer)
current_input = layer
previous_layer_output = layer_dictionary['output']
return architecture_built, current_input, regularization_cost
def test_forward_backward_1_no_activation(self):
x = np.arange(6).reshape(3, 2)
x_variable = Variable(x)
fc = FullyConnectedLayer(2, 1, Identity, x_variable)
w = fc._w._value.copy()
b = fc._b._value.copy()
wxb_desired = x @ w + b
wxb_actual = fc.forward()
np.testing.assert_almost_equal(wxb_actual, wxb_desired)
fc.backward(np.array([[6.0], [7.0], [8.0]]))
dl_dw_actual = fc._w.get_gradient()
dl_dx_actual = x_variable.get_gradient()
dl_dw_desired = np.array([[0 * 6 + 2 * 7 + 4 * 8], [1 * 6 + 3 * 7 + 5 * 8]])
dl_dx_desired = np.array([[w[0,0] * 6, w[1,0] * 6], [w[0,0] * 7, w[1,0] * 7], [w[0,0] * 8, w[1,0] * 8]])
np.testing.assert_allclose(dl_dw_actual, dl_dw_desired)
np.testing.assert_allclose(dl_dx_actual, dl_dx_desired)
dl_db_actual = fc._b.get_gradient()
dl_db_desired = np.array([6 + 7 + 8])
np.testing.assert_allclose(dl_db_actual, dl_db_desired)
def __init__(self,
in_dim,
hl_sizes,
out_dim,
isClassification=True,
learning_rate=.0001):
self.X = tf.placeholder(dtype=tf.float32,
shape=[None, in_dim],
name="X")
self.Y = tf.placeholder(dtype=tf.float32,
shape=[None, out_dim],
name="Y")
self.blocks = []
self.learning_rate = learning_rate
self.isClassification = isClassification
# Create Layers / Blocks
idim = in_dim
self.blocks.append(FullyConnectedLayer(
idim, hl_sizes[0])) # Non Res FC Layer
idim = hl_sizes[0]
self.blocks.append(
FullyConnectedResNetBlock(idim, hl_sizes, bias=False)) # Res Block
self.blocks.append(
FullyConnectedLayer(hl_sizes[-1], out_dim,
activation_fun=None)) # Non Res Block
# Roll On Through Those Tensors, Cowboy
Z = self.X
for i in range(len(self.blocks)):
Z = self.blocks[i].forward(Z)
self.Y_pred = Z
# Now setup cost function
if self.isClassification:
self.Yk = tf.nn.softmax(self.Y_pred, axis=1)
self.cost = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits_v2(labels=self.Y,
logits=self.Y_pred,
dim=1))
self.optimizer = tf.train.AdamOptimizer(
self.learning_rate).minimize(self.cost)
#self.optimizer = tf.train.AdagradDAOptimizer(.001).minimize(self.cost)
else: # Is Regressive
self.cost = tf.reduce_sum(
tf.squared_difference(self.Y_pred, self.Y))
self.optimizer = tf.train.AdamOptimizer(
self.learning_rate).minimize(self.cost)
# Start the session
self.set_session(tf.Session())
self.sess.run(tf.global_variables_initializer())
print(
"Session Initialized: Network Params will be dumped to CNN_Parameters.txt"
)
def __init__(self, input_dim, output_dim, hidden_layer_sizes = [64], activation_fun = tf.nn.relu, use_res_net_blocks = True, training_rate = 1e-3):
self.X = tf.placeholder(tf.float32, shape=[None,input_dim], name = "inputs")
self.Y = tf.placeholder(tf.float32, shape =[None,], name = "actions")
self.hidden_layers = []
self.input_dim = input_dim
self.output_dim = output_dim
self.training_rate = training_rate
idim = input_dim
self.hl_sizes = hidden_layer_sizes
self.experiences = {'states':[], 'values': []}
self.min_experiences = 100
self.max_expereinces = 1000
self.batch_size = 32
self.Xs = []
self.Ys = []
if use_res_net_blocks: # Then fill out tensorboard with res net
for i in range(len(hl_sizes)):
if i == 0:
self.hidden_layers.append(FullyConnectedLayer(idim, hl_sizes[i], activation_fun = activation_fun))
else:
self.hidden_layers.append(FullyConnectedResNetBlock(idim, [hl_sizes[i]], activation_fun = activation_fun, batch_normalization = False))
idim = hl_sizes[i]
else: # Use regular fully connected layers
for hl in self.hl_sizes:
self.hidden_layers.append(FullyConnectedLayer(idim, hl, activation_fun = activation_fun))
idim = hl
# Computes the Value of the current state
self.h_last = FullyConnectedLayer(idim, output_dim, activation_fun = None)
# Graph abstraction
Z = self.X
for hl in self.hidden_layers:
Z = hl.forward(Z)
self.Y_pred = self.h_last.forward(Z)
self.Y_pred = tf.reshape(self.Y_pred, [-1])
# Cost
self.cost = tf.reduce_sum(tf.square(self.Y - self.Y_pred))
self.train_op = tf.train.AdamOptimizer(self.training_rate).minimize(self.cost)
def buildNetwork(self):
"""
Builds the neural network with a fixed structure,
and a variable number of outputs.
"""
self.inputLayer = InputLayer()
convLayer = ConvolutionalLayer(5,10)
poolLayer = PoolingLayer(4)
reluLayer = ReluLayer()
convLayer2 = ConvolutionalLayer(4,20)
pool2Layer = PoolingLayer(2)
flattenLayer = FlattenLayer()
reluLayer2 = ReluLayer()
fullLayer = FullyConnectedLayer(20)
self.outputLayer = OutputLayer(self.numOutputs)
fullLayer.connect(self.outputLayer)
flattenLayer.connect(fullLayer)
reluLayer2.connect(flattenLayer)
pool2Layer.connect(reluLayer2)
convLayer2.connect(pool2Layer)
reluLayer.connect(convLayer2)
poolLayer.connect(reluLayer)
convLayer.connect(poolLayer)
self.inputLayer.connect(convLayer)
def test_forward_backward_4_no_activation(self):
x = np.arange(6).reshape(3, 2)
x_variable = Variable(x)
fc = FullyConnectedLayer(2, 4, Identity, x_variable)
w = fc._w._value.copy()
b = fc._b._value.copy()
wxb_desired = x @ w + b
wxb_actual = fc.forward()
np.testing.assert_almost_equal(wxb_actual, wxb_desired)
dl_dxwb = np.arange(6, 6 + 3 * 4).reshape(3, 4)
fc.backward(dl_dxwb)
dl_dw_actual = fc._w.get_gradient()
dl_dw_desired = np.array([
[x[:, 0].T @ dl_dxwb[:, 0], x[:, 0].T @ dl_dxwb[:, 1], x[:, 0].T @ dl_dxwb[:, 2],
x[:, 0].T @ dl_dxwb[:, 3]],
[x[:, 1].T @ dl_dxwb[:, 0], x[:, 1].T @ dl_dxwb[:, 1], x[:, 1].T @ dl_dxwb[:, 2],
x[:, 1].T @ dl_dxwb[:, 3]],
])
np.testing.assert_allclose(dl_dw_actual, dl_dw_desired)
def __init__(self, input_dim = 2,
output_dim = 1, hl_sizes = [64,64,64],
use_res_net = True, activation_fun = tf.nn.relu,
training_rate = 1e-3, eps = 1.0):
self.input_dim = input_dim
self.output_dim = output_dim
self.X = tf.placeholder(tf.float32, shape=[None,input_dim], name = "inputs")
self.actions = tf.placeholder(tf.float32, shape =[None,], name = "actions")
self.advantages = tf.placeholder(tf.float32, shape=[None,], name = "advantages")
self.hidden_layers = []
self.training_rate = training_rate
self.min_experiences = 100
self.max_experiences = 1000
self.batch_size = 32
self.inner_counter = 0.0
self.eps = eps
self.experiences = {'states': [], 'actions': [], 'advantages': []}
idim = input_dim
if use_res_net: # Then fill out tensorboard with res net
for i in range(len(hl_sizes)):
if i == 0:
self.hidden_layers.append(FullyConnectedLayer(idim, hl_sizes[i], activation_fun = activation_fun))
else:
self.hidden_layers.append(FullyConnectedResNetBlock(idim, [hl_sizes[i]], activation_fun = activation_fun, batch_normalization = False))
idim = hl_sizes[i]
else: # Use regular fully connected layers
for hl in hl_sizes:
self.hidden_layers.append(FullyConnectedLayer(idim, hl, activation_fun = activation_fun))
idim = hl
# last layer is Regressive to single node -- One for Mean, One for Std_dev
self.Y_mean = FullyConnectedLayer(idim, output_dim, activation_fun = None)
self.Y_std = FullyConnectedLayer(idim, output_dim, activation_fun= tf.nn.relu)
# Rollout Abstraction
Z = self.X
for hl in self.hidden_layers:
Z = hl.forward(Z)
#Mean
mean = tf.reshape(self.Y_mean.forward(Z), [-1])
std = tf.reshape(self.Y_std.forward(Z), [-1]) + 1e-5
# Sample from the normal distribution
norm = tf.contrib.distributions.Normal(mean, std)
self.predict_op = tf.clip_by_value(norm.sample(), -1,1)
log_probs = norm.log_prob(self.actions)
self.cost = -tf.reduce_sum(self.advantages * log_probs + .1*norm.entropy())
self.train_op = tf.train.AdamOptimizer(self.training_rate).minimize(self.cost)
theano.config.floatX = 'float32'
else:
print ("Running with a CPU. If this is not desired, then the modify "+\
"network3.py to set\nthe GPU flag to True.")
training_data, validation_data, test_data = load_data_shared()
mini_batch_size = 10
from Network import Network
from FullyConnectedLayer import FullyConnectedLayer
from SoftmaxLayer import SoftmaxLayer
from ConvPoolLayer import ConvPoolLayer
net = Network([
FullyConnectedLayer(n_in=784, n_out=100),
SoftmaxLayer(n_in=100, n_out=10)
], mini_batch_size)
net.SGD(training_data, 1, mini_batch_size, 0.1, validation_data, test_data)
# add a convolutional layer:
net = Network([
ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
filter_shape=(20, 1, 5, 5),
poolsize=(2, 2)),
FullyConnectedLayer(n_in=20*12*12, n_out=100),
SoftmaxLayer(n_in=100, n_out=10)],
mini_batch_size)
from Network import Network
from FullyConnectedLayer import FullyConnectedLayer
import numpy as np
X = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]])
y = np.array([[0],[1],[1],[0]])
layers = [
FullyConnectedLayer(X.shape[1], 4, False, 'sigmoid'),
FullyConnectedLayer(4, 1, True, 'sigmoid')
]
net = Network(8000, layers)
net.train(X, y)
import numpy as np from FullyConnectedLayer import FullyConnectedLayer from OutputLayer import OutputLayer l1 = FullyConnectedLayer(17*17) l2 = FullyConnectedLayer(50) l3 = OutputLayer(10) l1.append(l2).append(l3) l1.forward(np.ones(17*17)) print l1.getUnits() print l2.getUnits() print l3.getUnits() l3.setTrainData(np.array([1,0,0,0,0,0,0,0,0,0])) l3.backward()
return np.sum(a) / float(X.shape[0]) * 100.
def one_hot(x, size):
a = np.zeros((x.shape[0], size))
a[np.arange(x.shape[0]), x] = 1.
return a
if __name__ == '__main__':
batch_size = 20
# A simple strided convnet
layers = [
ConvolutionLayer((4, 4, 1, 20), strides=2, activation=lkrelu, filter_init=lambda shp: np.random.normal(size=shp) * np.sqrt(1.0 / (28*28 + 13*13*20)) ),
ConvolutionLayer((5, 5, 20, 40), strides=2, activation=lkrelu, filter_init=lambda shp: np.random.normal(size=shp) * np.sqrt(1.0 / (13*13*20 + 5*5*40)) ),
FlattenLayer((5, 5, 40)),
FullyConnectedLayer((5*5*40, 100), activation=sigmoid, weight_init=lambda shp: np.random.normal(size=shp) * np.sqrt(1.0 / (5*5*40 + 100.))),
FullyConnectedLayer((100, 10), activation=linear, weight_init=lambda shp: np.random.normal(size=shp) * np.sqrt(1.0 / (110.)))
]
lr = 0.001
k = 2000
net = CNNetwork(layers, learning_rate=lr, loss_function=cross_entropy)
(train_data_X, train_data_Y), v, (tx, ty) = mnist_loader.load_data('./data/mnist.pkl.gz')
train_data_Y = one_hot(train_data_Y, size=10)
ty = one_hot(ty, size=10)
train_data_X = np.reshape(train_data_X, [-1, 28, 28, 1])
tx = np.reshape(tx, [-1, 28, 28, 1])
for epoch in range(100000):
shuffled_index = np.random.permutation(train_data_X.shape[0])
batch_train_X = train_data_X[shuffled_index[:batch_size]]
testdata = []
for i in numbers:
number_path_train = os.path.join(dataset_path, 'train', str(i))
for filename in os.listdir(number_path_train):
traindata.append((os.path.join(dataset_path, 'train', str(i), filename), i))
number_path_test = os.path.join(dataset_path, 'test', str(i))
for filename in os.listdir(number_path_test):
testdata.append((os.path.join(dataset_path, 'test', str(i), filename), i))
random.shuffle(traindata)
random.shuffle(testdata)
lr = 0.01
l1 = FullyConnectedLayer(28*28, lr)
l2 = FullyConnectedLayer(30, lr)
l3 = OutputLayer(10, lr)
l1.append(l2).append(l3)
if mode == 'train':
n_iter = 3
for j in range(0, n_iter):
i = 0
for (image_path, label) in traindata:
im_pil = Image.open(image_path)
im = np.asarray(im_pil, dtype=np.float)
im = im / 255
class PolicyModel:
def __init__(self, input_dim = 2,
output_dim = 1, hl_sizes = [64,64,64],
use_res_net = True, activation_fun = tf.nn.relu,
training_rate = 1e-3, eps = 1.0):
self.input_dim = input_dim
self.output_dim = output_dim
self.X = tf.placeholder(tf.float32, shape=[None,input_dim], name = "inputs")
self.actions = tf.placeholder(tf.float32, shape =[None,], name = "actions")
self.advantages = tf.placeholder(tf.float32, shape=[None,], name = "advantages")
self.hidden_layers = []
self.training_rate = training_rate
self.min_experiences = 100
self.max_experiences = 1000
self.batch_size = 32
self.inner_counter = 0.0
self.eps = eps
self.experiences = {'states': [], 'actions': [], 'advantages': []}
idim = input_dim
if use_res_net: # Then fill out tensorboard with res net
for i in range(len(hl_sizes)):
if i == 0:
self.hidden_layers.append(FullyConnectedLayer(idim, hl_sizes[i], activation_fun = activation_fun))
else:
self.hidden_layers.append(FullyConnectedResNetBlock(idim, [hl_sizes[i]], activation_fun = activation_fun, batch_normalization = False))
idim = hl_sizes[i]
else: # Use regular fully connected layers
for hl in hl_sizes:
self.hidden_layers.append(FullyConnectedLayer(idim, hl, activation_fun = activation_fun))
idim = hl
# last layer is Regressive to single node -- One for Mean, One for Std_dev
self.Y_mean = FullyConnectedLayer(idim, output_dim, activation_fun = None)
self.Y_std = FullyConnectedLayer(idim, output_dim, activation_fun= tf.nn.relu)
# Rollout Abstraction
Z = self.X
for hl in self.hidden_layers:
Z = hl.forward(Z)
#Mean
mean = tf.reshape(self.Y_mean.forward(Z), [-1])
std = tf.reshape(self.Y_std.forward(Z), [-1]) + 1e-5
# Sample from the normal distribution
norm = tf.contrib.distributions.Normal(mean, std)
self.predict_op = tf.clip_by_value(norm.sample(), -1,1)
log_probs = norm.log_prob(self.actions)
self.cost = -tf.reduce_sum(self.advantages * log_probs + .1*norm.entropy())
self.train_op = tf.train.AdamOptimizer(self.training_rate).minimize(self.cost)
def set_session(self, sess):
self.session = sess
def partial_fit(self, X, actions, advantages, printOp = True):
#X = np.atleast_2d(X)
actions = np.atleast_1d(actions)
advantages = np.atleast_1d(advantages)
#print(X, actions, advantages)
self.experiences['actions'].append(actions)
self.experiences['advantages'].append(advantages)
self.experiences['states'].append(X)
if len(self.experiences['advantages']) < self.min_experiences:
return
if len(self.experiences['advantages']) > self.max_experiences:
self.experiences['advantages'].pop(0)
self.experiences['actions'].pop(0)
self.experiences['states'].pop(0)
indxs = np.random.choice(len(self.experiences['advantages']), self.batch_size)
#print("INDEXES: ", indxs)
states = [self.experiences['states'][indx] for indx in indxs]
states = np.reshape(np.array(states), (self.batch_size, self.input_dim))
actions = [self.experiences['actions'][indx] for indx in indxs]
actions = np.reshape(actions, (self.batch_size,))
advantages = [self.experiences['advantages'][indx] for indx in indxs]
advantages = np.reshape(advantages, (self.batch_size,))
loss, _ = self.session.run([self.cost, self.train_op], feed_dict={self.X: states, self.actions: actions, self.advantages: advantages})
if printOp:
print("Computed Partial Fit with Loss of: ", loss)
def predict(self, X):
X = np.atleast_2d(X)
return self.session.run(self.predict_op, feed_dict={self.X: X})
def sample_action(self, X):
return np.asscalar(self.predict(X)[0])
import numpy as np
from NeuralNetwork import NeuralNetwork
from FullyConnectedLayer import FullyConnectedLayer
from ActivationLayer import ActivationLayer
from ActivationFunctions import tanh, tanhDerivative
from LossFunction import meanSquaredError, meanSquaredErrorDerivative
# Sample training data
inputData = np.array([[[0, 0]], [[0, 1]], [[1, 0]], [[1, 1]]])
expectedOutput = np.array([[[0]], [[1]], [[1]], [[0]]])
# Creating a neural network with 3 nodes in first hidden layer, 1 node in final layer, with activation functions
# after each layer
network = NeuralNetwork()
network.add(FullyConnectedLayer(2, 3))
network.add(ActivationLayer(tanh, tanhDerivative))
network.add(FullyConnectedLayer(3, 1))
network.add(ActivationLayer(tanh, tanhDerivative))
# Training network
network.setLoss(meanSquaredError, meanSquaredErrorDerivative)
network.train(inputData, expectedOutput, epochs=1000, learningRate=.1)
# Test the network
output = network.predict(inputData)
for set in range(len(inputData)):
print("For set {} my prediction is {}. The correct value is {}".format(
inputData[set], output[set], expectedOutput[set]))
def __init__(self, input_dims, output_dim,
fc_hl_sizes, isClassification = True, use_resnet = True ):
self.isClassification = isClassification
self.output_dim = output_dim
self.input_dims = input_dims
# Implement VGG Blocks for a Mini - VGG Architecture
# Begin Graph Abstraction
self.CNN_Block_Layers = []
self.FC_Layers = []
x_dims = [None] + list(input_dims)
# Make sure the user did not say the image dim is 2D -> 2D should be changes to at least 3d
assert len(input_dims) == 3 # Should be inputted as a 3D image (if 2D -> third channel is 1)
self.X = tf.placeholder(dtype = tf.float32, shape = x_dims, name = "X")
self.Y = tf.placeholder(dtype = tf.float32, shape = [None, output_dim], name = "Y")
# Holds the individual layer's pool size and pool stride settings
self.pool_stride_block_settings = {'Num_Pools': [], 'Conv_Stride': [] ,'Pool_Stride': []}
# OPTION 1:
# Stack Convolutional Blocks -- VGG
if not use_resnet:
ConvBlock = CNNBlocks.VGGConvPoolBlock64()
self.CNN_Block_Layers.append(ConvBlock)
ConvBlock = CNNBlocks.VGGConvPoolBlock64()
self.CNN_Block_Layers.append(ConvBlock)
ConvBlock = CNNBlocks.VGGConvPoolBlock128()
self.CNN_Block_Layers.append(ConvBlock)
ConvBlock = CNNBlocks.VGGConvPoolBlock256()
self.CNN_Block_Layers.append(ConvBlock)
ConvBlock = CNNBlocks.VGGConvPoolBlock512()
self.CNN_Block_Layers.append(ConvBlock)
ConvBlock = CNNBlocks.VGGConvPoolBlock512()
self.CNN_Block_Layers.append(ConvBlock)
else:
# OPTION 2:
# Stack Convolutional Blocks -- ResNet
ConvBlock = CNNBlocks.VGGConvPoolBlock64()
self.CNN_Block_Layers.append(ConvBlock)
ConvBlock = CNNBlocks.ResNetBlock128()
self.CNN_Block_Layers.append(ConvBlock)
ConvBlock = CNNBlocks.ResNetBlock128()
self.CNN_Block_Layers.append(ConvBlock)
ConvBlock = CNNBlocks.ResNetBlock128()
self.CNN_Block_Layers.append(ConvBlock)
ConvBlock = CNNBlocks.VGGConvPoolBlock128()
self.CNN_Block_Layers.append(ConvBlock)
# Determine Number of Stacked Convolutional Blocks
self.num_conv_blocks = len(self.CNN_Block_Layers)
# Initialize the Convolutional weights
idim = self.input_dims[2]
for i in range(self.num_conv_blocks):
# Change the input of a block layer to have the same input channel dimention as the inputted image / feature map channel
self.CNN_Block_Layers[i].set_layer_input_channel_dim(idim) #(0,idim) # Layer zero or 1
idim = self.CNN_Block_Layers[i].get_layer_output_channel_dim() # Layer Zero or Layer 1 ( we choose layer one to connect to the next block)
self.pool_stride_block_settings['Num_Pools'].append(self.CNN_Block_Layers[i].get_num_pools())
self.pool_stride_block_settings['Pool_Stride'].append(self.CNN_Block_Layers[i].get_block_pool_stride())
# Store Pool Stride information
#self.pool_stride_block_settings['Num_Pools'].append(ConvBlock.get_num_pools())
#self.pool_stride_block_settings['Pool_Stride'].append(ConvBlock.get_block_pool_stride())
# Get input size for the fully connected layer
FC_INPUT_SIZE = int(self.get_FC_input_size(input_dims,
self.pool_stride_block_settings['Num_Pools'],
self.pool_stride_block_settings['Pool_Stride']))
idim = FC_INPUT_SIZE
# Report FC Size for Conv - NN transition
print("Fully Connected Input Size: ", idim)
for i in range(len(fc_hl_sizes)):
self.FC_Layers.append(FullyConnectedLayer(idim, fc_hl_sizes[i]))
idim = fc_hl_sizes[i]
self.FC_Layers.append(FullyConnectedLayer(idim, self.output_dim, activation_fun = None))
# Rollout the network tensor abstraction
Z = self.X
# Convolutional Rollout
for i in range(self.num_conv_blocks):
Z = self.CNN_Block_Layers[i].forward(Z)
# Reshape Z for the Fully Connected Rollout
Z = tf.reshape(Z,(-1,FC_INPUT_SIZE))
# Fully Conneccted Rollout
for i in range(len(fc_hl_sizes)):
Z = self.FC_Layers[i].forward(Z)
self.Y_pred = self.FC_Layers[-1].forward(Z)
# Here we either take the linear output unit, or we use the CNN for classification
# If the classifcation flag is set to true, we use a softmax cross entropy with logits cost function
if self.isClassification:
self.Yk = tf.nn.softmax(self.Y_pred, axis = 1)
self.cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits_v2(labels = self.Y, logits = self.Y_pred, dim = 1))
self.optimizer = tf.train.AdamOptimizer(.001).minimize(self.cost)
#self.optimizer = tf.train.AdagradDAOptimizer(.001).minimize(self.cost)
else: # Is Regressive
self.cost = tf.reduce_sum(tf.squared_difference(self.Y_pred, self.Y))
self.optimizer = tf.train.AdamOptimizer(.001).minimize(self.cost)
# Start the session
self.set_session(tf.Session())
self.sess.run(tf.global_variables_initializer())
print("Session Initialized: Network Params will be dumped to CNN_Parameters.txt")
def testGradient():
"""Test the backprop implementation by checking the gradients on a small network"""
# load the training data
images, labels = load_mnist()
images /= 255.0
grad_images = images[:,:,0:10] #use 10 image subset for gradient checking
grad_labels = labels[0,0:10] #respective labels for the images--going to have to encode these labels
# create a small network, 1 conv layer + 1 pooling layer + 1 fully connected softmax
# convolutional layer, taking in a 28x28 image, using 2 9x9 filters
# output should be 2 28-9+1x28-9+1 = 2 20x20 feature maps in a (20, 20, 2) form
layer0 = ConvLayer(grad_images[:,:,0].reshape((28,28,1)), (28, 28, 1), (9, 9, 2, 1))
print "initalized convolutional layer"
layer0.forwardprop(grad_images[:,:,0].reshape((28,28,1)))
print "finished forward pass of convolutional layer"
# pooling layer, taking in 2 20x20 feature maps
# output should be 2 10x10 feature maps (though may want to downsample 5x for gradient check)
layer1 = PoolingLayer(layer0.output, (20, 20, 2))
print "initialized pooling layer"
layer1.downsample(layer0.output, (20, 20, 2))
print "finished forward pass of pooling layer"
# fully-connected softmax layer, taking in 2 10x10 feature maps (if downsampled by 2)
# or taking in 2 4x4 feature maps (if downsampled by 5)
# either way, flattened into a long input vector
full_conn_input = layer1.output.flatten()
layer2 = FullyConnectedLayer(full_conn_input.reshape((full_conn_input.size, 1)), full_conn_input.size, 10)
print "initialized fully-conn layer"
layer2.softmax_output(full_conn_input.reshape((full_conn_input.size, 1)))
print "finished forward pass of fully-conn layer"
# perform backpropagation
target = np.zeros((10,1))
for i in range(0, 10):
if grad_labels[i] == 1:
target[i] = 1
layer2.backprop(0, 0, target)
print "finished layer 2 backprop"
layer1.upsample(layer2, 0)
print "finished layer 1 backprop"
layer0.backprop(layer1)
print "finished layer 0 backprop"
# # after initialization, finish training
# for i in range(1, grad_labels.size):
# # forward propagation
# layer0.forwardprop(grad_images[:,:,i].reshape((28,28,1)))
# layer1.downsample(layer0.output, (20,20,2))
# full_conn_input = layer1.output.flatten()
# layer2.softmax_output(full_conn_input.reshape((full_conn_input.size, 1)))
#
# # backpropagation
# target = np.zeros((10,1))
# for j in range(0,10):
# if grad_labels[i] == 1:
# target[i] = 1
# layer2.backprop(0, 0, target)
# layer1.upsample(layer2, 0)
# layer0.backprop(layer1)
# check the gradient
epsilon = 1.0e-4
layer0_check = layer0
layer1_check = layer1
layer2_check = layer2
layer0_w_vec = layer0.W.flatten()
layer0_bias_vec = layer0.bias.flatten()
layer0_gradw = layer0.gradient_w.flatten()
layer0_gradb = layer0.gradient_b.flatten()
layer2_w_vec = layer2.W.flatten()
layer2_bias_vec = layer2.bias.flatten()
layer2_gradw = layer2.gradient_w.flatten()
layer2_gradb = layer2.gradient_b.flatten()
w_vec = np.concatenate((layer0_w_vec, layer0_bias_vec, layer2_w_vec, layer2_bias_vec))
backprop_vec = np.concatenate((layer0_gradw, layer0_gradb, layer2_gradw, layer2_gradb))
print layer0_gradw
gradient_check = np.zeros(w_vec.size)
for i in range(0, w_vec.size):
pos = w_vec
pos[i] += epsilon
neg = w_vec
neg[i] -= epsilon
# feed-forward to get J(w+e), J(w-e), subtract and calculate gradient
# J(w+e)
layer0_check.W = pos[0:layer0_w_vec.size].reshape(layer0.filter_shape)
layer0_check.bias = pos[layer0_w_vec.size : layer0_w_vec.size+layer0_bias_vec.size].reshape(layer0.bias_shape)
layer2_check.W = pos[layer0_w_vec.size+layer0_bias_vec.size : layer0.W.size+layer0.bias.size+layer2_w_vec.size].reshape(layer2.W.shape)
layer2_check.bias = pos[layer0.W.size+layer0.bias.size+layer2_w_vec.size:].reshape(layer2.bias.shape)
layer0_check.forwardprop(grad_images[:,:,0].reshape((28,28,1)))
layer1_check.downsample(layer0_check.output, (20,20,2))
full_conn_input = layer1.output.flatten()
layer2_check.softmax_output(full_conn_input.reshape((full_conn_input.size, 1)))
pos_out = J(layer2_check.output, grad_labels[0])
# J(w-e)
layer0_check.W = neg[0:layer0_w_vec.size].reshape(layer0.filter_shape)
layer0_check.bias = neg[layer0_w_vec.size : layer0_w_vec.size+layer0_bias_vec.size].reshape(layer0.bias_shape)
layer2_check.W = neg[layer0_w_vec.size+layer0_bias_vec.size : layer0.W.size+layer0.bias.size+layer2_w_vec.size].reshape(layer2.W.shape)
layer2_check.bias = neg[layer0.W.size+layer0.bias.size+layer2_w_vec.size:].reshape(layer2.bias.shape)
layer0_check.forwardprop(grad_images[:,:,0].reshape((28,28,1)))
layer1_check.downsample(layer0_check.output, (20,20,2))
full_conn_input = layer1.output.flatten()
layer2_check.softmax_output(full_conn_input.reshape((full_conn_input.size, 1)))
neg_out = J(layer2_check.output, grad_labels[0])
# compute gradient for i
gradient_check[i] = (pos_out - neg_out)/(2*epsilon)
# print gradient_check
print gradient_check[0:layer0_w_vec.size]
def SGD_train(minibatch_size, data, labels, alpha, momentum, epochs):
"""Train the network with stochastic gradient descent
:type minibatch_size: an integer
:param minibatch_size: the size of the minibatches (usually something like 256)
:type data: 3D matrix height x width x num training data pts.
:param data: A 3D matrix that contains all of the training data points of the set
:type labels: num training data pts x 1 vector
:param labels: the labels for each image
:type alpha: float
:param alpha: the learning rate
:type momentum: float
:param momentum: the momentum
:type epochs: an integer
:param epochs: the number of epochs (ie. iterations) through the training
"""
it = 0
# convolutional layer, taking in a 28x28 image, using 2 9x9 filters
# output should be 2 28-9+1x28-9+1 = 2 20x20 feature maps in a (20, 20, 2) form
layer0 = ConvLayer((28, 28, 1), (9,9,2))
print "initialized convolutional layer"
# pooling layer, taking in 2 20x20 feature maps
# output should be 2 10x10 feature maps
layer1 = PoolingLayer((20, 20, 2))
print "initialized pooling layer"
# fully-connected softmax layer, taking in 2 10x10 feature maps (if downsampled by 2)
# flattened into a long input vector
layer2 = FullyConnectedLayer(200, 10)
print "initialized fully-connected layer"
params = np.concatenate((layer0.W.flatten(), layer0.bias.flatten(), layer2.W.flatten(), layer2.bias.flatten()))
velocity = np.zeros(params.shape)
for i in range(0, epochs):
correct_class = 0
cost = 0.0
# shuffle the dataset--shuffle_vec will be used as indices
shuffle_vec = rand.permutation(data.shape[2])
for j in range(0, data.shape[2] - minibatch_size + 1, minibatch_size):
# perform gradient descent w/each batch
it += 1
if it == 20:
# increase momentum after 20 iterations
momentum = 0.9
# gradient should be an unrolled vector of the avg. sum of the 256 gradients gotten
# from the forward pass and backprop
for k in range(0, minibatch_size):
layer0.forwardprop(data[:,:,shuffle_vec[j+k]].reshape((28,28,1)))
layer1.downsample(layer0.output, (20,20,2))
layer2_input = layer1.output.flatten()
layer2.softmax_output(layer2_input.reshape((layer2_input.size, 1)))
cost += J(layer2.output, labels[shuffle_vec[j+k]])
# print "%d %d" % (np.argmax(layer2.output), labels[shuffle_vec[j+k]])
if np.argmax(layer2.output) == labels[shuffle_vec[j+k]]:
correct_class += 1
# backprop
layer2.backprop(0, 0, encode_label(labels[shuffle_vec[j+k]]))
layer1.upsample(layer2, 0)
layer0.backprop(layer1)
# flatten the gradient vector
if k == 0:
grad = np.concatenate((layer0.gradient_w.flatten(), layer0.gradient_b.flatten(), layer2.gradient_w.flatten(), layer2.gradient_b.flatten()))
else:
grad += np.concatenate((layer0.gradient_w.flatten(), layer0.gradient_b.flatten(), layer2.gradient_w.flatten(), layer2.gradient_b.flatten()))
grad /= minibatch_size
# update velocity vector
velocity = momentum*velocity + alpha*grad
params = params - velocity
# update the parameters
layer0.W = params[0:layer0.W.flatten().size].reshape(layer0.W.shape)
next_begin = layer0.W.flatten().size
layer0.bias = params[next_begin:next_begin+layer0.bias.flatten().size].reshape(layer0.bias.shape)
next_begin += layer0.bias.flatten().size
layer2.W = params[next_begin:next_begin+layer2.W.flatten().size].reshape(layer2.W.shape)
next_begin += layer2.W.flatten().size
layer2.bias = params[next_begin:].reshape(layer2.bias.shape)
# reduce learning rate by half after each epoch
alpha /= 2.0
print "%d correct classifications" % correct_class
print "cost function is ", cost/(minibatch_size*(data.shape[2] - minibatch_size + 1))
class ValueModel:
def __init__(self, input_dim, output_dim, hidden_layer_sizes = [64], activation_fun = tf.nn.relu, use_res_net_blocks = True, training_rate = 1e-3):
self.X = tf.placeholder(tf.float32, shape=[None,input_dim], name = "inputs")
self.Y = tf.placeholder(tf.float32, shape =[None,], name = "actions")
self.hidden_layers = []
self.input_dim = input_dim
self.output_dim = output_dim
self.training_rate = training_rate
idim = input_dim
self.hl_sizes = hidden_layer_sizes
self.experiences = {'states':[], 'values': []}
self.min_experiences = 100
self.max_expereinces = 1000
self.batch_size = 32
self.Xs = []
self.Ys = []
if use_res_net_blocks: # Then fill out tensorboard with res net
for i in range(len(hl_sizes)):
if i == 0:
self.hidden_layers.append(FullyConnectedLayer(idim, hl_sizes[i], activation_fun = activation_fun))
else:
self.hidden_layers.append(FullyConnectedResNetBlock(idim, [hl_sizes[i]], activation_fun = activation_fun, batch_normalization = False))
idim = hl_sizes[i]
else: # Use regular fully connected layers
for hl in self.hl_sizes:
self.hidden_layers.append(FullyConnectedLayer(idim, hl, activation_fun = activation_fun))
idim = hl
# Computes the Value of the current state
self.h_last = FullyConnectedLayer(idim, output_dim, activation_fun = None)
# Graph abstraction
Z = self.X
for hl in self.hidden_layers:
Z = hl.forward(Z)
self.Y_pred = self.h_last.forward(Z)
self.Y_pred = tf.reshape(self.Y_pred, [-1])
# Cost
self.cost = tf.reduce_sum(tf.square(self.Y - self.Y_pred))
self.train_op = tf.train.AdamOptimizer(self.training_rate).minimize(self.cost)
def set_session(self, sess):
self.session = sess
def partial_fit(self, X, Y, printOp = True):
self.experiences['states'].append(X)
self.experiences['values'].append(Y)
if len(self.experiences['states']) < self.min_experiences:
return
if len(self.experiences['states']) > self.max_expereinces:
self.experiences['states'].pop(0)
self.experiences['values'].pop(0)
indxs = np.random.choice(len(self.experiences['states']), self.batch_size)
#print("INDEXES: ", indxs)
X = [self.experiences['states'][indx] for indx in indxs]
X = np.reshape(np.array(X), (self.batch_size, self.input_dim))
Y = [self.experiences['values'][indx] for indx in indxs]
Y = np.reshape(np.array(Y), (self.batch_size,))
loss, _ = self.session.run([self.cost, self.train_op], feed_dict = {self.X: X, self.Y: Y})
if printOp:
print("Partial Fit Loss is: ", loss)
def predict(self, X):
return self.session.run(self.Y_pred, feed_dict = {self.X: X})
