class InitialColumnProgNN(object):
"""
Descr: Initial network to train for later use transfer learning with a
Progressive Neural Network.
Args:
topology - A list of number of units in each hidden dimension.
First entry is input dimension.
activations - A list of activation functions to use on the transforms.
session - A TensorFlow session.
Returns:
None - attaches objects to class for InitialColumnProgNN.session.run()
"""
def __init__(self,
topology,
activations,
session,
checkpoint_base_path,
dtype=tf.float32):
n_input = topology[0]
# Layers in network.
L = len(topology) - 1
self.session = session
self.L = L
self.topology = topology
self.checkpoint_base_path = checkpoint_base_path
self.o_n = tf.placeholder(dtype,
shape=[None, n_input],
name='prog_nn_input_placeholder')
self.W = []
self.b = []
self.h = [self.o_n]
params = []
for k in range(L):
shape = topology[k:k + 2]
self.W.append(
weight_variable(shape, name="weight_var_layer_" + str(k)))
self.b.append(
bias_variable([shape[1]], name="bias_var_layer_" + str(k)))
self.h.append(activations[k](tf.matmul(self.h[-1], self.W[k]) +
self.b[k]))
params.append(self.W[-1])
params.append(self.b[-1])
self.pc = ParamCollection(self.session, params)
def save(self, checkpoint_i):
save_path = get_checkpoint_path(self.checkpoint_base_path, 0,
checkpoint_i)
current_params = self.pc.get_values_flat()
np.save(save_path, current_params)
def restore_weights(self, checkpoint_i):
save_path = get_checkpoint_path(self.checkpoint_base_path, 0,
checkpoint_i)
saved_theta = np.load(save_path)
self.pc.set_values_flat(saved_theta)
class ExtensibleColumnProgNN(object):
"""
Descr: An extensible network column for use in transfer learning with a
Progressive Neural Network.
Args:
topology - A list of number of units in each hidden dimension.
First entry is input dimension.
activations - A list of activation functions to use on the transforms.
session - A TensorFlow session.
prev_columns - Previously trained columns, either Initial or Extensible,
we are going to create lateral connections to for the current column.
Returns:
None - attaches objects to class for ExtensibleColumnProgNN.session.run()
"""
def __init__(self,
topology,
activations,
session,
checkpoint_base_path,
prev_columns,
dtype=tf.float32):
n_input = topology[0]
self.topology = topology
self.session = session
width = len(prev_columns)
# Layers in network. First value is n_input, so it doesn't count.
L = len(topology) - 1
self.L = L
self.prev_columns = prev_columns
self.checkpoint_base_path = checkpoint_base_path
self.column_number = width
# Doesn't work if the columns aren't the same height.
assert all([self.L == x.L for x in prev_columns])
self.o_n = tf.placeholder(dtype,
shape=[None, n_input],
name='prog_nn_input_placeholder')
self.W = [[]] * L
self.b = [[]] * L
self.U = []
for k in range(L - 1):
self.U.append([[]] * width)
self.h = [self.o_n]
# Collect parameters to hand off to ParamCollection.
params = []
for k in range(L):
W_shape = topology[k:k + 2]
self.W[k] = weight_variable(W_shape,
name="weight_var_layer_" + str(k))
self.b[k] = bias_variable([W_shape[1]],
name="bias_var_layer_" + str(k))
if k == 0:
self.h.append(activations[k](tf.matmul(self.h[-1], self.W[k]) +
self.b[k]))
params.append(self.W[k])
params.append(self.b[k])
continue
preactivation = tf.matmul(self.h[-1], self.W[k]) + self.b[k]
for kk in range(width):
U_shape = [prev_columns[kk].topology[k], topology[k + 1]]
# Remember len(self.U) == L - 1!
self.U[k - 1][kk] = weight_variable(
U_shape,
name="lateral_weight_var_layer_" + str(k) + "_to_column_" +
str(kk))
# pprint(prev_columns[kk].h[k].get_shape().as_list())
# pprint(self.U[k-1][kk].get_shape().as_list())
preactivation += tf.matmul(prev_columns[kk].h[k],
self.U[k - 1][kk])
self.h.append(activations[k](preactivation))
params.append(self.W[k])
params.append(self.b[k])
for kk in range(width):
params.append(self.U[k - 1][kk])
self.pc = ParamCollection(self.session, params)
def save(self, checkpoint_i):
save_path = get_checkpoint_path(self.checkpoint_base_path,
self.column_number, checkpoint_i)
current_params = self.pc.get_values_flat()
np.save(save_path, current_params)
def restore_weights(self, checkpoint_i):
save_path = get_checkpoint_path(self.checkpoint_base_path,
self.column_number, checkpoint_i)
saved_theta = np.load(save_path)
self.pc.set_values_flat(saved_theta)
class InitialColumnProgNN(object):
"""
Descr: Initial network to train for later use transfer learning with a
Progressive Neural Network.
Args:
n_input - The array length which the input image is flattened to
kernel - A list of kernel size for each layer
activations - A list of activation functions to use on the transforms.
session - A TensorFlow session.
checkpoing_base_path - Save path.
Returns:
None - attaches objects to class for InitialColumnProgNN.session.run()
"""
#todo:add name to enery tensor
def __init__(self,
n_input,
kernel,
stride,
activations,
session,
checkpoint_base_path,
dtype=tf.float32):
# Layers in network.
self.session = session
#self.L = len(topology)
#self.topology = topology
self.o_n = tf.placeholder(dtype=tf.float32, shape=[None, n_input])
self.imageIn = tf.reshape(self.o_n, shape=[-1, 84, 84, 1])
self.checkpoint_base_path = checkpoint_base_path
self.W = []
self.b = []
self.h = [self.imageIn]
params = []
padding = 'SAME'
#The first two layers
for k in range(2):
#When training on second column, if the previous weights need to be frozen, set initial=True,
# the variables are set as not trainable.
self.W.append(weight_variable(kernel[k], initial=None))
self.b.append(bias_variable([kernel[k][-1]], initial=None))
conv = tf.nn.conv2d(self.h[-1], self.W[k], stride[k],
padding) + self.b[k]
self.h.append(activations(conv))
params.append(self.W[k])
params.append(self.b[k])
self.h.append(tf.layers.flatten(self.h[-1]))
#fully connected layer
self.W.append(weight_variable(kernel[-1], initial=None))
self.b.append(bias_variable([kernel[-1][-1]], initial=None))
fc = tf.matmul(self.h[-1], self.W[-1]) + self.b[-1]
self.h.append(activations(fc))
params.append(self.W[-1])
params.append(self.b[-1])
#Calculate value
self.W.append(weight_variable([256, 1], initial=None))
self.b.append(bias_variable([1], initial=None))
self.value = tf.matmul(self.h[-1], self.W[-1]) + self.b[-1]
params.append(self.W[-1])
params.append(self.b[-1])
#Calculate policy
self.W.append(weight_variable([256, 6], initial=None))
self.b.append(bias_variable([6], initial=None))
fc = tf.matmul(self.h[-1], self.W[-1]) + self.b[-1]
self.policy = tf.nn.softmax(fc)
params.append(self.W[-1])
params.append(self.b[-1])
self.pc = ParamCollection(self.session, params)
def add_input_to_feed_dict(self, feed_dict, input_batch):
feed_dict[self.o_n] = input_batch
return feed_dict
def save(self, checkpoint_i):
self.save_path, file_name = get_checkpoint_path(
self.checkpoint_base_path, 0, checkpoint_i)
current_params = self.pc.get_values_flat()
np.save(file_name, current_params)
def restore_weights(self, checkpoint_i):
self.save_path, file_name = get_checkpoint_path(
self.checkpoint_base_path, 0, checkpoint_i)
saved_theta = np.load(file_name)
self.pc.set_values_flat(saved_theta)
class ExtensibleColumnProgNN(object):
"""
Descr: An extensible network column for use in transfer learning with a
Progressive Neural Network.
Args:
n_input - The array length which the input image is flattened to
kernel - A list of kernel size for each layer
activations - A list of activation functions to use on the transforms.
session - A TensorFlow session.
checkpoing_base_path - Save path.
prev_columns - Previously trained columns, either Initial or Extensible,
we are going to create lateral connections to for the current column.
Returns:
None - attaches objects to class for ExtensibleColumnProgNN.session.run()
"""
def __init__(self,
n_input,
kernel,
stride,
activations,
session,
checkpoint_base_path,
prev_columns,
dtype=tf.float32):
self.session = session
self.width = len(prev_columns)
# Layers in network. First value is n_input, so it doesn't count.
L = 5
self.prev_columns = prev_columns
self.checkpoint_base_path = checkpoint_base_path
# Doesn't work if the columns aren't the same height
#assert all([L == x.L for x in prev_columns])
self.o_n = tf.placeholder(dtype=tf.float32, shape=[None, n_input])
self.imageIn = tf.reshape(self.o_n, shape=[-1, 84, 84, 1])
self.W = [[]] * L
self.b = [[]] * L
self.U = []
self.V = []
self.a = []
for k in range(L - 1):
self.U.append([[]] * self.width)
self.V.append([[]] * self.width)
self.a.append([[]] * self.width)
self.h = [self.imageIn] #h[0]
# Collect parameters to hand off to ParamCollection.
params = []
padding = 'SAME'
#first layer, not connected with previous layers
self.W[0] = (weight_variable(kernel[0]))
self.b[0] = (bias_variable([kernel[0][-1]]))
conv = tf.nn.conv2d(self.h[-1], self.W[0], stride[0],
padding) + self.b[0]
self.h.append(activations(conv)) #h[1]
params.append(self.W[0])
params.append(self.b[0])
#second layer
self.W[1] = (weight_variable(kernel[1]))
self.b[1] = (bias_variable([kernel[1][-1]]))
preactivation = tf.nn.conv2d(self.h[-1], self.W[1], stride[1],
padding) + self.b[1]
for kk in range(self.width):
self.a[0][kk] = adapters()
ah = tf.multiply(self.a[0][kk], prev_columns[kk].h[1])
maps_in = ah.get_shape().as_list()[3]
maps_out = int(maps_in / (2.0 * self.width))
self.V[0][kk] = weight_variable([1, 1, maps_in, maps_out])
lateral = tf.nn.conv2d(ah, self.V[0][kk], stride[2], padding)
lateral = activations(lateral)
self.U[0][kk] = weight_variable(
[kernel[1][0], kernel[1][1], maps_out, kernel[1][3]])
preactivation1 = tf.nn.conv2d(lateral, self.U[0][kk], stride[1],
padding)
preactivation = preactivation + preactivation1
self.h.append(activations(preactivation))
params.append(self.W[1])
params.append(self.b[1])
for kk in range(self.width):
params.append(self.U[0][kk])
params.append(self.V[0][kk])
params.append(self.a[0][kk])
self.h.append(tf.layers.flatten(self.h[-1])) #h[3]
#fully connected layer
self.W[2] = (weight_variable(kernel[-1]))
self.b[2] = (bias_variable([kernel[-1][-1]]))
fc = tf.matmul(self.h[-1], self.W[2]) + self.b[2]
for kk in range(self.width):
self.a[1][kk] = adapters()
ah = tf.multiply(self.a[1][kk], prev_columns[kk].h[2])
maps_in = ah.get_shape().as_list()[3]
maps_out = int(maps_in / (2.0 * self.width))
self.V[1][kk] = weight_variable([1, 1, maps_in, maps_out])
lateral = tf.nn.conv2d(ah, self.V[1][kk], stride[2], padding)
lateral = activations(lateral)
#lateral = tf.reshape(lateral,[-1,kernel[-1][-1]])
lateral = tf.layers.flatten(lateral)
self.U[1][kk] = weight_variable(
[lateral.get_shape().as_list()[-1], kernel[-1][-1]])
fc += tf.matmul(lateral, self.U[1][kk])
self.h.append(activations(fc)) #h[4]
params.append(self.W[2])
params.append(self.b[2])
for kk in range(self.width):
params.append(self.U[1][kk])
params.append(self.V[1][kk])
params.append(self.a[1][kk])
#calculate value
self.W[3] = (weight_variable([256, 1]))
self.b[3] = (bias_variable([1]))
self.value = tf.matmul(self.h[-1], self.W[3]) + self.b[3]
for kk in range(self.width):
self.a[2][kk] = adapters()
ah = tf.multiply(self.a[2][kk], prev_columns[kk].h[4])
maps_in = ah.get_shape().as_list()[1]
maps_out = int(maps_in / (2.0 * self.width))
self.V[2][kk] = weight_variable([maps_in, maps_out])
lateral = tf.matmul(ah, self.V[2][kk])
lateral = activations(lateral)
self.U[2][kk] = weight_variable([maps_out, 1])
self.value += tf.matmul(lateral, self.U[2][kk])
params.append(self.W[3])
params.append(self.b[3])
for kk in range(self.width):
params.append(self.U[2][kk])
params.append(self.V[2][kk])
params.append(self.a[2][kk])
#calculate policy
self.W[4] = (weight_variable([256, 6]))
self.b[4] = (bias_variable([6]))
fc = tf.matmul(self.h[-1], self.W[4]) + self.b[4]
for kk in range(self.width):
self.a[3][kk] = adapters()
ah = tf.multiply(self.a[3][kk], prev_columns[kk].h[4])
maps_in = ah.get_shape().as_list()[1]
maps_out = int(maps_in / (2.0 * self.width))
self.V[3][kk] = weight_variable([maps_in, maps_out])
lateral = tf.matmul(ah, self.V[3][kk])
lateral = activations(lateral)
self.U[3][kk] = weight_variable([maps_out, 6])
fc += tf.matmul(lateral, self.U[3][kk])
self.policy = tf.nn.softmax(fc)
params.append(self.W[4])
params.append(self.b[4])
for kk in range(self.width):
params.append(self.U[3][kk])
params.append(self.V[3][kk])
params.append(self.a[3][kk])
self.pc = ParamCollection(self.session, params)
def add_input_to_feed_dict(self, feed_dict, input_batch):
for col in self.prev_columns:
feed_dict[col.o_n] = input_batch
feed_dict[self.o_n] = input_batch
return feed_dict
def save(self, checkpoint_i):
self.save_path, file_name = get_checkpoint_path(
self.checkpoint_base_path, self.width, checkpoint_i)
current_params = self.pc.get_values_flat()
np.save(file_name, current_params)
def restore_weights(self, checkpoint_i):
self.save_path, file_name = get_checkpoint_path(
self.checkpoint_base_path, self.width, checkpoint_i)
saved_theta = np.load(file_name)
self.pc.set_values_flat(saved_theta)
