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During the training procedure, I want to calculate the moving maximum(and minimum) values of a batch feature maps and then I will implement quantization alogrithm based on the moving max (or min) values. For example: moving_max = (1-momentum)x(previous moving_max) + momentum x (current max value of a batch).
I implement the following codes based on the customized tf2.keras.layer:
from tensorflow.keras.layers import Layer
class QATQuantizerLayer(Layer):
def __init__(self, num_bits, momentum=0.01, **kwargs):
super(QATQuantizerLayer, self).__init__(**kwargs)
self.num_bits = num_bits
self.momentum = momentum
self.num_flag = 0
self.quant_min_val = 0
self.quant_max_val = (1 << self.num_bits) - 1
self.quant_range = float(self.quant_max_val - self.quant_min_val)
def build(self, input_shape):
self.moving_min = self.add_weight("moving_min", shape=(1,), initializer=tf.constant_initializer(-6), trainable=False)
self.moving_max = self.add_weight("moving_max", shape=(1,), initializer=tf.constant_initializer(6), trainable=False)
return super(QATQuantizerLayer, self).build(input_shape)
def call(self, inputs, training, **kwargs):
if training is None:
training = False
if training == True:
batch_min = tf.reduce_min(inputs)
batch_max = tf.reduce_max(inputs)
if self.num_flag == 0:
self.num_flag += 1
self.moving_min = batch_min
self.moving_max = batch_max
else:
temp_min = (1 - self.momentum) * self.moving_min + self.momentum * batch_min
temp_max = (1 - self.momentum) * self.moving_max + self.momentum * batch_max
self.moving_min = temp_min
self.moving_max = temp_max
float_range = self.moving_max - self.moving_min
scale = float_range / self.quant_range
scale = tf.maximum(scale, tf.keras.backend.epsilon())
zero_point = tf.math.round(self.moving_min / scale)
output = (tf.clip_by_value(_round_imp(inputs / scale) - zero_point,
self.quant_min_val, self.quant_max_val) + zero_point) * scale
return output
However, when I start to train I get the following errors:
TypeError: An op outside of the function building code is being passed a "Graph" tensor. It is possible to have Graph tensors leak out of the function building context by including a tf.init_scope in your function building code. For example, the following function will fail:......
If I change the following statement: [temp_min = (1 - self.momentum) * self.moving_min + self.momentum * batch_min] to [temp_min = (1 - self.momentum) + self.momentum * batch_min], the error is disappeared. (That is, remove self.moving_min from the statement)
How can I solve this problem?
Thank you very much.
I am trying to implement the PPO algorithm with clipped loss in addition to KL penalties and run training on Mujuco Gym environments. After ~ 15000 gradient steps, policy collapses into returning NaN.
These are the policy training info before the policy collapses:
A: tf.Tensor(-0.10426917, shape=(), dtype=float32)
LOG_A: tf.Tensor(37.021107, shape=(), dtype=float32)
LOSS: tf.Tensor(0.16812761, shape=(), dtype=float32)
GRAD: tf.Tensor(
[[-3.4624012e-04 -1.2807851e-04 -1.9778654e-01 ... -2.7586846e+00
-1.2552655e-01 -1.7212760e-03]
[ 4.6312678e-05 -2.2251482e-04 5.5088173e-03 ... 9.5249921e-02
2.2186586e-03 2.0080474e-04]
[ 2.0314787e-05 -1.6381161e-04 7.1509695e-03 ... 1.1740552e-01
3.4010289e-03 1.2105847e-04]
...
[ 1.7827883e-04 -1.1712313e-05 5.8873045e-01 ... 9.2354174e+00
2.9186043e-01 -2.2818900e-03]
[-9.0385452e-05 3.0951984e-03 -3.6487404e-02 ... -2.6829168e-01
-3.9602429e-02 2.0654879e-03]
[ 2.2925157e-04 4.6892464e-03 5.9946489e-01 ... 9.3497839e+00
3.0514282e-01 -1.3834883e-03]], shape=(11, 256), dtype=float32)
A: tf.Tensor(nan, shape=(), dtype=float32)
LOG_A: tf.Tensor(nan, shape=(), dtype=float32)
Note: The gradient info captures only the gradients of the first layer, as I have found capturing all gradient info to be messy and seemingly redundant.
What I have tried:
Tuning hyperparameters: I have tried multiple sets of hyperparameters including the one documented in the original paper. The same error occurs(the hyperparams setup provided in the example below are chosen for higher sampling efficiency for faster debugging).
Gradient clipping: Gradient norm has been clipped to be unitary, and as shown above, it does not appear to have the exploding gradient issue.
Guaranteed numerical stability of tanh squashing of policy log probability: A small epsilon was used to clip the sum of squares so that action log probability does not return inf after tanh squashing.
Unitized code example:
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import gym
import scipy.signal
import time
from tensorflow.keras import Model
import matplotlib.pyplot as plt
import random
import tensorflow_probability as tfp
tf.keras.backend.set_floatx('float32')
EPSILON = 1e-10
################## GLOBAL SETUP P1 ##################
problem = "Hopper-v2"
env = gym.make(problem)
eval_env = gym.make(problem)
num_states = env.observation_space.shape[0]
print("Size of State Space -> {}".format(num_states), flush=True)
num_actions = env.action_space.shape[0]
print("Size of Action Space -> {}".format(num_actions), flush=True)
upper_bound = env.action_space.high[0]
lower_bound = env.action_space.low[0]
print("Max Value of Action -> {}".format(upper_bound), flush=True)
print("Min Value of Action -> {}".format(lower_bound), flush=True)
minibatch_size = 256
##########*****####################*****##########
#################### Auxiliaries ####################
def discounted_cumulative_sums(x, discount):
# Discounted cumulative sums of vectors for computing rewards-to-go and advantage estimates
return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], axis=0)[::-1]
##########*****####################*****##########
#################### Replay Buffer ####################
class Buffer:
def __init__(self, observation_dimensions, action_dimensions, size, gamma=0.99, lam=0.95):
self.observation_buffer = np.zeros(
(size, observation_dimensions), dtype=np.float32
)
self.action_buffer = np.zeros((size, action_dimensions), dtype=np.int32)
self.advantage_buffer = np.zeros(size, dtype=np.float32)
self.reward_buffer = np.zeros(size, dtype=np.float32)
self.return_buffer = np.zeros(size, dtype=np.float32)
self.value_buffer = np.zeros(size, dtype=np.float32)
self.logprobability_buffer = np.zeros(size, dtype=np.float32)
self.gamma, self.lam = gamma, lam
self.pointer, self.trajectory_start_index = 0, 0
def store(self, observation, action, reward, value, logprobability):
self.observation_buffer[self.pointer] = observation
self.action_buffer[self.pointer] = action
self.reward_buffer[self.pointer] = reward
self.value_buffer[self.pointer] = value
self.logprobability_buffer[self.pointer] = logprobability
self.pointer += 1
def finish_trajectory(self, last_value=0):
path_slice = slice(self.trajectory_start_index, self.pointer)
rewards = np.append(self.reward_buffer[path_slice], last_value)
values = np.append(self.value_buffer[path_slice], last_value)
deltas = rewards[:-1] + self.gamma * values[1:] - values[:-1]
self.advantage_buffer[path_slice] = discounted_cumulative_sums(
deltas, self.gamma * self.lam
)
self.return_buffer[path_slice] = discounted_cumulative_sums(
rewards, self.gamma
)[:-1]
self.trajectory_start_index = self.pointer
def get(self):
# Get all data of the buffer and normalize the advantages
rindex = np.random.choice(self.pointer, minibatch_size)
advantage_mean, advantage_std = (
np.mean(self.advantage_buffer[rindex]),
np.std(self.advantage_buffer[rindex]),
)
return (
self.observation_buffer[rindex],
self.action_buffer[rindex],
(self.advantage_buffer[rindex] - advantage_mean) / advantage_std,
self.return_buffer[rindex],
self.logprobability_buffer[rindex],
)
def clear(self):
self.pointer, self.trajectory_start_index = 0, 0
##########*****####################*****##########
#################### Models ####################
class Actor(Model):
def __init__(self):
super().__init__()
self.action_dim = num_actions
self.dense1_layer = layers.Dense(256, activation="relu")
self.dense2_layer = layers.Dense(256, activation="relu")
self.mean_layer = layers.Dense(self.action_dim)
self.stdev_layer = layers.Dense(self.action_dim)
def call(self, state, eval_mode=False):
a1 = self.dense1_layer(state)
a2 = self.dense2_layer(a1)
mu = self.mean_layer(a2)
log_sigma = self.stdev_layer(a2)
sigma = tf.exp(log_sigma)
covar_m = tf.linalg.diag(sigma**2)
dist = tfp.distributions.MultivariateNormalTriL(loc=mu, scale_tril=tf.linalg.cholesky(covar_m))
if eval_mode:
action_ = mu
else:
action_ = dist.sample()
action = tf.tanh(action_)
log_pi_ = dist.log_prob(action_)
log_pi = log_pi_ - tf.reduce_sum(tf.math.log(tf.clip_by_value(1 - action**2, EPSILON, 1.0)), axis=1)
return action*upper_bound, log_pi
def get_critic():
state_input = layers.Input(shape=(num_states))
state_out = layers.Dense(256, activation="relu")(state_input)
out = layers.Dense(256, activation="relu")(state_out)
outputs = layers.Dense(1, dtype='float32')(out)
model = tf.keras.Model(state_input, outputs)
return model
##########*****####################*****##########
#################### GLOBAL SETUP P2 ####################
# Hyperparameters of the PPO algorithm
horizon = 2048
iterations = 2000
gamma = 0.99
clip_ratio = 0.2
epochs = 500
lam = 0.97
target_kl = 0.01
beta = 1.0
render = False
actor_model = Actor()
critic_model = get_critic()
lr = 0.0003
policy_optimizer = tf.keras.optimizers.Adam(learning_rate=lr,
# )
clipnorm=1.0)
value_optimizer = tf.keras.optimizers.Adam(learning_rate=lr,
# )
clipnorm=1.0)
buffer = Buffer(num_states, num_actions, horizon)
##########*****####################*****##########
#################### Training ####################
observation, episode_return, episode_length = env.reset(), 0, 0
tf_observation = tf.expand_dims(observation, 0)
def train_policy(
observation_buffer, action_buffer, logprobability_buffer, advantage_buffer
):
global beta
with tf.GradientTape() as tape: # Record operations for automatic differentiation.
action, log_a = actor_model(observation_buffer)
# print("A: ", tf.reduce_mean(action))
# print("LOG_A: ", tf.reduce_mean(log_a))
ratio = tf.exp(
log_a
- logprobability_buffer
)
# print("R: ", tf.reduce_mean(ratio), flush=True)
cd_ratio = tf.clip_by_value(ratio, (1 - clip_ratio), (1 + clip_ratio))
min_advantage = cd_ratio * advantage_buffer
_kl = -beta*tf.math.reduce_max(logprobability_buffer - log_a)
policy_loss = -tf.reduce_mean(tf.minimum(ratio * advantage_buffer, min_advantage) + _kl)
# print("LOSS: ", policy_loss)
policy_grads = tape.gradient(policy_loss, actor_model.trainable_variables)
policy_optimizer.apply_gradients(zip(policy_grads, actor_model.trainable_variables))
# print("GRAD: ", policy_grads[0], flush=True)
action_opt, log_a_opt = actor_model(observation_buffer)
kl = tf.reduce_mean(
logprobability_buffer
- log_a_opt
)
if kl < target_kl/1.5:
beta = beta/2
if kl > target_kl*1.5:
beta = beta*2
return kl
def train_value_function(observation_buffer, return_buffer):
with tf.GradientTape() as tape: # Record operations for automatic differentiation.
value_loss = tf.reduce_mean((return_buffer - critic_model(observation_buffer)) ** 2)
value_grads = tape.gradient(value_loss, critic_model.trainable_variables)
value_optimizer.apply_gradients(zip(value_grads, critic_model.trainable_variables))
for ite in range(iterations):
for t in range(horizon):
if render:
env.render()
action, log_pi_a = actor_model(tf_observation)
action = action[0]
observation_new, reward, done, _ = env.step(action)
episode_return += reward
episode_length += 1
value_t = critic_model(tf_observation)
buffer.store(observation, action, reward, value_t, log_pi_a)
observation = observation_new
tf_observation = tf.expand_dims(observation, 0)
terminal = done
if terminal or (t == horizon - 1):
last_value = 0 if done else critic_model(tf_observation)
buffer.finish_trajectory(last_value)
observation, episode_return, episode_length = env.reset(), 0, 0
tf_observation = tf.expand_dims(observation, 0)
for _ in range(epochs):
(
observation_buffer,
action_buffer,
advantage_buffer,
return_buffer,
logprobability_buffer,
) = buffer.get()
kl = train_policy(
observation_buffer, action_buffer, logprobability_buffer, advantage_buffer
)
train_value_function(observation_buffer, return_buffer)
buffer.clear()
##########*****####################*****##########
Note:
The code base is constructed by a combination of a modified version of the official keras PPO tutorial(https://keras.io/examples/rl/ppo_cartpole/) and Modules(Mainly the policy network) that have been tested in other implementations.
I refrained from using tf_function declaration as I am very new to tensorflow, thus not understanding its impact, and I have read from various github issues that sometimes such declaration causes numerical instability due to caching. However, it could be a source of my issues.
Any help is appreciated, and apologies if something is missing or unclear.
I am trying to build a metric that is comparable to the metrics.PrecisionAtRecall class. Therefore, I've tried to build a custom metric by extending the keras.metrics.Metric class.
The original function is WSS = (TN + FN)/N − 1 + TP/(TP + FN) and this should be calculated at a certain recall value, for say 95%.
What I have until now is the following:
class WorkSavedOverSamplingAtRecall(tf.keras.metrics.Metric):
def __init__(self, recall, name='wss_at_recall', **kwargs):
super(WorkSavedOverSamplingAtRecall, self).__init__(name=name, **kwargs)
self.wss = self.add_weight(name='wss', initializer='zeros')
def update_state(self, y_true, y_pred, sample_weight=None):
y_pred_pos = tf.cast(backend.round(backend.clip(y_pred, 0, 1)), tf.float32)
y_pred_neg = 1 - y_pred_pos
y_pos = tf.cast(backend.round(backend.clip(y_true, 0, 1)), tf.float32)
y_neg = 1 - y_pos
fn = backend.sum(y_neg * y_pred_pos)
tn = backend.sum(y_neg * y_pred_neg)
tp = backend.sum(y_pos * y_pred_pos)
n = len(y_true) # number of studies in batch
r = tp/(tp+fn+backend.epsilon()) # recall
self.wss.assign(((tn+fn)/n)-(1+r))
def result(self):
return self.wss
def reset_states(self):
# The state of the metric will be reset at the start of each epoch.
self.wss.assign(0.)
How can I calculate the WSS at a certain recall? I've seen the following in tensorflow's own git repository:
def __init__(self, recall, num_thresholds=200, name=None, dtype=None):
if recall < 0 or recall > 1:
raise ValueError('`recall` must be in the range [0, 1].')
self.recall = recall
self.num_thresholds = num_thresholds
super(PrecisionAtRecall, self).__init__(
value=recall,
num_thresholds=num_thresholds,
name=name,
dtype=dtype)
But that is't really possible through the keras.metrics.Metric class
If we follow the definition of the WSS#95 given by this paper :Reducing Workload in Systematic Review Preparation Using Automated Citation Classification, then we have
For the present work, we have fixed recall at 0.95 and therefore work saved over sampling at 95% recall (WSS#95%) is:
And you could define your update function by :
class WorkSavedOverSamplingAtRecall(tf.keras.metrics.Metric):
def __init__(self, recall, name='wss_at_recall', **kwargs):
if recall < 0 or recall > 1:
raise ValueError('`recall` must be in the range [0, 1].')
self.recall = recall
super(WorkSavedOverSamplingAtRecall, self).__init__(name=name, **kwargs)
self.wss = self.add_weight(name='wss', initializer='zeros')
def update_state(self, y_true, y_pred, sample_weight=None):
y_pred_pos = tf.cast(backend.round(backend.clip(y_pred, 0, 1)), tf.float32)
y_pred_neg = 1 - y_pred_pos
y_neg = 1 - y_pos
fn = backend.sum(y_neg * y_pred_pos)
tn = backend.sum(y_neg * y_pred_neg)
n = len(y_true) # number of studies in batch
self.wss.assign(((tn+fn)/n)-(1-self.recall))
One other solution would be to extend from the tensorflow class SensitivitySpecificityBase and to implement the WSS as the PresicionAtRecall class is implemented.
By using this class, here's how the WSS is calculated :
Compute the recall at all the thresholds (200 thresholds by default).
Find the index of the threshold where the recall is closest to the requested value. (0.95 in that case).
Compute the WSS at that index.
The number of thresholds is use to match the given recall.
import tensorflow as tf
from tensorflow.python.keras.metrics import SensitivitySpecificityBase
class WorkSavedOverSamplingAtRecall(SensitivitySpecificityBase):
def __init__(self, recall, num_thresholds=200, name="wss_at_recall", dtype=None):
if recall < 0 or recall > 1:
raise ValueError('`recall` must be in the range [0, 1].')
self.recall = recall
self.num_thresholds = num_thresholds
super(WorkSavedOverSamplingAtRecall, self).__init__(
value=recall, num_thresholds=num_thresholds, name=name, dtype=dtype
)
def result(self):
recalls = tf.math.div_no_nan(
self.true_positives, self.true_positives + self.false_negatives
)
n = self.true_negatives + self.true_positives + self.false_negatives + self.false_positives
wss = tf.math.div_no_nan(
self.true_negatives+self.false_negatives, n
)
return self._find_max_under_constraint(
recalls, wss, tf.math.greater_equal
)
def get_config(self):
"""For serialization purposes"""
config = {'num_thresholds': self.num_thresholds, 'recall': self.recall}
base_config = super(WorkSavedOverSamplingAtRecall, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
I am working on an artifical neural network which I have created via subclassing.
The subclassing looks like this:
import time
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import scipy.stats as si
import sympy as sy
from sympy.stats import Normal, cdf
from sympy import init_printing
class DGMNet(tf.keras.Model):
def __init__(self, n_layers, n_nodes, dimensions=1):
"""
Parameters:
- n_layers: number of layers
- n_nodes: number of nodes in (inner) layers
- dimensions: number of spacial dimensions
"""
super().__init__()
self.n_layers = n_layers
self.initial_layer = DenseLayer(dimensions + 1, n_nodes, activation="relu")
self.lstmlikelist = []
for _ in range(self.n_layers):
self.lstmlikelist.append(LSTMLikeLayer(dimensions + 1, n_nodes, activation="relu"))
self.final_layer = DenseLayer(n_nodes, 1, activation=None)
def call(self, t, x):
X = tf.concat([t,x], 1)
S = self.initial_layer.call(X)
for i in range(self.n_layers):
S = self.lstmlikelist[i].call({'S': S, 'X': X})
result = self.final_layer.call(S)
return result
class DenseLayer(tf.keras.layers.Layer):
def __init__(self, n_inputs, n_outputs, activation):
"""
Parameters:
- n_inputs: number of inputs
- n_outputs: number of outputs
- activation: activation function
"""
super(DenseLayer, self).__init__()
self.n_inputs = n_inputs
self.n_outputs = n_outputs
self.W = self.add_weight(shape=(self.n_inputs, self.n_outputs),
initializer='random_normal',
trainable=True)
self.b = self.add_weight(shape=(1, self.n_outputs),
initializer='random_normal',
trainable=True)
self.activation = _get_function(activation)
def call(self, inputs):
S = tf.add(tf.matmul(inputs, self.W), self.b)
S = self.activation(S)
return S
class LSTMLikeLayer(tf.keras.layers.Layer):
def __init__(self, n_inputs, n_outputs, activation):
"""
Parameters:
- n_inputs: number of inputs
- n_outputs: number of outputs
- activation: activation function
"""
super(LSTMLikeLayer, self).__init__()
self.n_outputs = n_outputs
self.n_inputs = n_inputs
self.Uz = self.add_variable("Uz", shape=[self.n_inputs, self.n_outputs])
self.Ug = self.add_variable("Ug", shape=[self.n_inputs, self.n_outputs])
self.Ur = self.add_variable("Ur", shape=[self.n_inputs, self.n_outputs])
self.Uh = self.add_variable("Uh", shape=[self.n_inputs, self.n_outputs])
self.Wz = self.add_variable("Wz", shape=[self.n_outputs, self.n_outputs])
self.Wg = self.add_variable("Wg", shape=[self.n_outputs, self.n_outputs])
self.Wr = self.add_variable("Wr", shape=[self.n_outputs, self.n_outputs])
self.Wh = self.add_variable("Wh", shape=[self.n_outputs, self.n_outputs])
self.bz = self.add_variable("bz", shape=[1, self.n_outputs])
self.bg = self.add_variable("bg", shape=[1, self.n_outputs])
self.br = self.add_variable("br", shape=[1, self.n_outputs])
self.bh = self.add_variable("bh", shape=[1, self.n_outputs])
self.activation = _get_function(activation)
def call(self, inputs):
S = inputs['S']
X = inputs['X']
Z = self.activation(tf.add(tf.add(tf.matmul(X, self.Uz), tf.matmul(S, self.Wz)), self.bz))
G = self.activation(tf.add(tf.add(tf.matmul(X, self.Ug), tf.matmul(S, self.Wg)), self.bg))
R = self.activation(tf.add(tf.add(tf.matmul(X, self.Ur), tf.matmul(S, self.Wr)), self.br))
H = self.activation(tf.add(tf.add(tf.matmul(X, self.Uh), tf.matmul(tf.multiply(S, R), self.Wh)), self.bh))
Snew = tf.add(tf.multiply(tf.subtract(tf.ones_like(G), G), H), tf.multiply(Z, S))
return Snew
def _get_function(name):
f = None
if name == "tanh":
f = tf.nn.tanh
elif name == "sigmoid":
f = tf.nn.sigmoid
elif name == "relu":
f = tf.nn.relu
elif not name:
f = tf.identity
assert f is not None
return f
# Sampling
def sampler(N1, N2, N3):
np.random.seed(42)
# Sampler #1: PDE domain
t1 = np.random.uniform(low=T0,
high=T,
size=[N1,1])
s1 = np.random.uniform(low=S1,
high=S2,
size=[N1,1])
# Sampler #2: boundary condition
t2 = np.zeros(shape=(1, 1))
s2 = np.zeros(shape=(1, 1))
# Sampler #3: initial/terminal condition
t3 = T * np.ones((N3,1)) #Terminal condition
s3 = np.random.uniform(low=S1,
high=S2,
size=[N3,1])
return (t1, s1, t2, s2, t3, s3)
# Loss function
def loss(model, t1, x1, t2, x2, t3, x3):
# Loss term #1: PDE
V = model(t1, x1)
V_t = tf.gradients(V, t1)[0]
V_x = tf.gradients(V, x1)[0]
V_xx = tf.gradients(V_x, x1)[0]
f = V_t + r*x1*V_x + 0.5*sigma**2*x1**2*V_xx - r*V
L1 = tf.reduce_mean(tf.square(f))
# Loss term #2: boundary condition
#L2 = tf.reduce_mean(tf.square(V))
# Loss term #3: initial/terminal condition
L3 = tf.reduce_mean(tf.square(model(t3, x3) - tf.math.maximum(x3-K,0)))
return (L1, L3)
# B-S's analytical known solution
def analytical_solution(t, x):
#C = SN(d1) - Xe- rt N(d2)
#S: spot price
#K: strike price
#T: time to maturity
#r: interest rate
#sigma: volatility of underlying asset
d1 = (np.log(x / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))
d2 = (np.log(x / K) + (r - 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))
call = (x * si.norm.cdf(d1, 0.0, 1.0) - K * np.exp(-r * T) * si.norm.cdf(d2, 0.0, 1.0))
return call
# Set random seeds
np.random.seed(42)
tf.random.set_seed(42)
# Strike price
K = 0.5
# PDE parameters
r = 0.05 # Interest rate
sigma = 0.25 # Volatility
# Time limits
T0 = 0.0 + 1e-10 # Initial time
T = 1.0 # Terminal time
# Space limits
S1 = 0.0 + 1e-10 # Low boundary
S2 = 1.0 # High boundary
# Number of samples
NS_1 = 1000
NS_2 = 0
NS_3 = 100
t1, s1, t2, s2, t3, s3 = sampler(NS_1, NS_2, NS_3)
Now what I want to do is to iterate over different parameters and create a new ann for each iteration.
My plan was to do it in this way:
tf.compat.v1.disable_eager_execution()
t1_t = tf.compat.v1.placeholder(tf.float32, [None,1])
x1_t = tf.compat.v1.placeholder(tf.float32, [None,1])
t2_t = tf.compat.v1.placeholder(tf.float32, [None,1])
x2_t = tf.compat.v1.placeholder(tf.float32, [None,1])
t3_t = tf.compat.v1.placeholder(tf.float32, [None,1])
x3_t = tf.compat.v1.placeholder(tf.float32, [None,1])
volatility_list = [0.08]#[0.08, 0.16, 0.18, 0.2, 0.28]
stages_list = [10]#, 50, 100]
layers_list = [3]#, 5, 7]
npl_list = [3]#, 6, 9, 12, 15]
for sigma in volatility_list:
for st in stages_list:
for lay in layers_list:
for npl in npl_list:
# Neural Network definition
num_layers = lay
nodes_per_layer = npl
ann = DGMNet(num_layers, nodes_per_layer)
L1_t, L3_t = loss(ann, t1_t, x1_t, t2_t, x2_t, t3_t, x3_t)
loss_t = L1_t + L3_t
# Optimizer parameters
global_step = tf.Variable(1, trainable=False)
starter_learning_rate = 0.001
learning_rate = tf.compat.v1.train.exponential_decay(starter_learning_rate, global_step,
100000, 0.96, staircase=True)
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss_t)
# Training parameters
steps_per_sample = st
sampling_stages = 100#2000
# Plot tensors
tplot_t = tf.compat.v1.placeholder(tf.float32, [None,1], name="tplot_t") # We name to recover it later
xplot_t = tf.compat.v1.placeholder(tf.float32, [None,1], name="xplot_t")
vplot_t = tf.identity(ann(tplot_t, xplot_t), name="vplot_t") # Trick for naming the trained model
# Training data holders
sampling_stages_list = []
elapsed_time_list = []
loss_list = []
L1_list = []
L3_list = []
# Train network!!
init_op = tf.compat.v1.global_variables_initializer()
sess = tf.compat.v1.Session()
sess.run(init_op)
for i in range(sampling_stages):
t1, x1, t2, x2, t3, x3 = sampler(NS_1, NS_2, NS_3)
start_time = time.clock()
for _ in range(steps_per_sample):
loss, L1, L3, _ = sess.run([loss_t, L1_t, L3_t, optimizer],
feed_dict = {t1_t:t1, x1_t:x1, t2_t:t2, x2_t:x2, t3_t:t3, x3_t:x3})
end_time = time.clock()
elapsed_time = end_time - start_time
sampling_stages_list.append(i)
elapsed_time_list.append(elapsed_time)
loss_list.append(loss)
L1_list.append(L1)
L3_list.append(L3)
text = "Stage: {:04d}, Loss: {:e}, L1: {:e}, L3: {:e}, {:f} seconds".format(i, loss, L1, L3, elapsed_time)
print(text)
#goodness of fit
time_0 = 0
listofzeros = [time_0] * 100
prices_for_goodness = np.linspace(S1,S2, 100)
goodness_list = []
solution_goodness = analytical_solution(listofzeros, prices_for_goodness)
ttt = time_0*np.ones_like(prices_for_goodness.reshape(-1,1))
nn_goodness, = sess.run([vplot_t],
feed_dict={tplot_t:ttt, xplot_t:prices_for_goodness.reshape(-1,1)})
deviation_list = np.abs(solution_goodness - nn_goodness)/(T-T0)
print("{0:.2f}%".format(np.average(deviation_list)*100))
Unfortunately as soon as it ends the first iteration I get a TypeError that 'numpy.float32' object is not callable
Error Traceback:
TypeError Traceback (most recent call last)
<ipython-input-14-bb14643d0c42> in <module>()
10
11
---> 12 L1_t, L3_t = loss(ann, t1_t, x1_t, t2_t, x2_t, t3_t, x3_t)
13 loss_t = L1_t + L3_t
14
TypeError: 'numpy.float32' object is not callable
I guess that the problem is with the creation of the placeholders, however I am not sure how to solve it. Maybe one of you can help me
Thanks in advance!
Chris
Did you create a variable called 'loss'? It seems that the loss function is redefined by a variable with the same name, so then python tries to call that variable as a function.
I want to implement a multilayer NN with backpropagation. I have been trying for days, but it simply does not work. It is extremely clear in my head how it is supposed to work, I have streamline my code to be as simple as possible but I can't do it. It's probably something stupid, but I cannot see it.
The implementation I have done is with an input layer of 784 (28x28), two (L) hidden layers of 300 and an output of 10 classes. I have a bias in every layer (except last...)
The output activation is softmax and the hidden activation is ReLU.
I use mini batches of 600 examples over a dataset of 60k examples with 50 to 500 epoches.
Here the core of my code:
Preparation:
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
L = 2
K = len(np.unique(train_labels))
lr = 0.001
nb_epochs = 50
node_per_hidden_layer = 300
nb_batches = 100
W = []
losses_test = []
X_train = np.reshape(train_images, (train_images.shape[0], train_images.shape[1]*train_images.shape[2]))
X_test = np.reshape(test_images, (test_images.shape[0], train_images.shape[1]*train_images.shape[2]))
Y_train = np.zeros((train_labels.shape[0], K))
Y_train[np.arange(Y_train.shape[0]), train_labels] = 1
Y_test = np.zeros((test_labels.shape[0], K))
Y_test[np.arange(Y_test.shape[0]), test_labels] = 1
W.append(np.random.normal(0, 0.01, (X_train.shape[1]+1, node_per_hidden_layer)))
for i in range(L-1):
W.append(np.random.normal(0, 0.01, (node_per_hidden_layer+1, node_per_hidden_layer)))
W.append(np.random.normal(0, 0.01, (node_per_hidden_layer+1, K)))
Helper function:
def softmax(z):
exp = np.exp(z - z.max(1)[:,np.newaxis])
return np.array(exp / exp.sum(1)[:,np.newaxis])
def softmax_derivative(z):
sm = softmax(z)
return sm * (1-sm)
def ReLU(z):
return np.maximum(z, 0)
def ReLU_derivative(z):
return (z >= 0).astype(int)
def get_loss(y, y_pred):
return -np.sum(y * np.log(y_pred))
fitting
def fit():
minibatch_size = len(X_train) // nb_batches
for epoch in range(nb_epochs):
permutaion = list(np.random.permutation(X_train.shape[0]))
X_shuffle = X_train[permutaion]
Y_shuffle = Y_train[permutaion]
print("Epoch----------------", epoch)
for batche in range(0, X_shuffle.shape[0], minibatch_size):
Z = [None] * (L + 2)
a = [None] * (L + 2)
delta = [None] * (L + 2)
X = X_train[batche:batche+minibatch_size]
Y = Y_shuffle[batche:batche+minibatch_size]
### forward propagation
a[0] = np.append(X, np.ones((minibatch_size, 1)), axis=1)
for i in range(L):
Z[i + 1] = a[i] # W[i]
a[i + 1] = np.append(ReLU(Z[i+1]), np.ones((minibatch_size, 1), dtype=int), axis=1)
Z[-1] = a[L] # W[L]
a[-1] = softmax(Z[-1])
### back propagation
delta[-1] = (Y - a[-1]) * softmax_derivative(Z[-1])
for i in range(L, 0, -1):
delta[i] = (delta[i+1] # W[i].T)[:,:-1] * ReLU_derivative(Z[i])
for i in range(len(W)):
g = a[i].T # delta[i+1] / minibatch_size
W[i] = W[i] + lr * g
get_loss_on_test()
loss
def get_loss_on_test():
Z_test = [None] * (L + 2)
a_test = [None] * (L + 2)
a_test[0] = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
for i in range(L):
Z_test[i + 1] = a_test[i] # W[i]
a_test[i + 1] = np.append(ReLU(Z_test[i+1]), np.ones((len(X_test), 1)), axis=1)
Z_test[-1] = a_test[L] # W[L]
a_test[-1] = softmax(Z_test[-1])
losses_test.append(get_loss(Y_test, a_test[-1]))
main
losses_test.clear()
fit()
plt.plot(losses_test)
plt.show()
If you want to see it in my notebook with an example of losses graph, here the link: https://github.com/beurnii/INF8225/blob/master/tp2/jpt.ipynb
If you want more details on my assignment, this is part 1b (page 2 for english):
https://github.com/beurnii/INF8225/blob/master/tp2/INF8225_TP2_2020.pdf