尝试循环创建人工神经网络时出现 TypeError
TypeError when trying to make a loop creating artificial neural networks
我正在研究我通过子类化创建的人工神经网络。
子类化如下所示：
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)
现在我想做的是迭代不同的参数并为每次迭代创建一个新的ann。
我的计划是这样做的：
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))
不幸的是，第一次迭代结束后，我得到一个 TypeError 'numpy.float32' object is not callable
错误回溯：
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
我想问题出在占位符的创建上，但是我不确定如何解决它。也许你们中的一个可以帮助我
提前致谢！
克里斯
您是否创建了一个名为 'loss' 的变量？似乎损失函数是由同名变量重新定义的，因此 python 尝试将该变量作为函数调用。
我正在研究我通过子类化创建的人工神经网络。
子类化如下所示：
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)
现在我想做的是迭代不同的参数并为每次迭代创建一个新的ann。 我的计划是这样做的：
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))
不幸的是，第一次迭代结束后，我得到一个 TypeError 'numpy.float32' object is not callable
错误回溯：
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
我想问题出在占位符的创建上，但是我不确定如何解决它。也许你们中的一个可以帮助我
提前致谢！ 克里斯
您是否创建了一个名为 'loss' 的变量？似乎损失函数是由同名变量重新定义的，因此 python 尝试将该变量作为函数调用。