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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 am trying to use the Triple-Loss technique to fine-tune an EfficientNet network for human Re-ID using Keras. Here is the code I am using:
This is the generator:
class SampleGen(object):
def __init__(self, file_class_mapping):
self.file_class_mapping = file_class_mapping
self.class_to_list_files = defaultdict(list)
self.list_all_files = list(file_class_mapping.keys())
self.range_all_files = list(range(len(self.list_all_files)))
for file, class_ in file_class_mapping.items():
self.class_to_list_files[class_].append(file)
self.list_classes = list(set(self.file_class_mapping.values()))
self.range_list_classes = range(len(self.list_classes))
self.class_weight = np.array([len(self.class_to_list_files[class_]) for class_ in self.list_classes])
self.class_weight = self.class_weight / np.sum(self.class_weight)
def get_sample(self):
class_idx = np.random.choice(self.range_list_classes, 1, p=self.class_weight)[0]
examples_class_idx = np.random.choice(range(len(self.class_to_list_files[self.list_classes[class_idx]])), 2)
positive_example_1, positive_example_2 = \
self.class_to_list_files[self.list_classes[class_idx]][examples_class_idx[0]], \
self.class_to_list_files[self.list_classes[class_idx]][examples_class_idx[1]]
negative_example = None
while negative_example is None or self.file_class_mapping[negative_example] == \
self.file_class_mapping[positive_example_1]:
negative_example_idx = np.random.choice(self.range_all_files, 1)[0]
negative_example = self.list_all_files[negative_example_idx]
return positive_example_1, negative_example, positive_example_2
def read_and_resize(filepath):
im = Image.open((filepath)).convert('RGB')
im = im.resize((image_size, image_size))
return np.array(im, dtype="float32")
def augment(im_array):
if np.random.uniform(0, 1) > 0.9:
im_array = np.fliplr(im_array)
return im_array
def gen(triplet_gen):
while True:
list_positive_examples_1 = []
list_negative_examples = []
list_positive_examples_2 = []
for i in range(batch_size):
positive_example_1, negative_example, positive_example_2 = triplet_gen.get_sample()
path_pos1 = join(path_train, positive_example_1)
path_neg = join(path_train, negative_example)
path_pos2 = join(path_train, positive_example_2)
positive_example_1_img = read_and_resize(path_pos1)
negative_example_img = read_and_resize(path_neg)
positive_example_2_img = read_and_resize(path_pos2)
positive_example_1_img = augment(positive_example_1_img)
negative_example_img = augment(negative_example_img)
positive_example_2_img = augment(positive_example_2_img)
list_positive_examples_1.append(positive_example_1_img)
list_negative_examples.append(negative_example_img)
list_positive_examples_2.append(positive_example_2_img)
A = preprocess_input(np.array(list_positive_examples_1))
B = preprocess_input(np.array(list_positive_examples_2))
C = preprocess_input(np.array(list_negative_examples))
label = None
yield {'anchor_input': A, 'positive_input': B, 'negative_input': C}, label
This is how I create the model:
def get_model():
base_model = efn.EfficientNetB3(weights='imagenet', include_top=False)
for layer in base_model.layers:
layer.trainable = False
x = base_model.output
x = Dropout(0.6)(x)
x = Dense(embedding_dim)(x)
x = Lambda(lambda x: K.l2_normalize(x, axis=1), name="enc_out")(x)
embedding_model = Model(base_model.input, x, name="embedding")
input_shape = (image_size, image_size, 3)
anchor_input = Input(input_shape, name='anchor_input')
positive_input = Input(input_shape, name='positive_input')
negative_input = Input(input_shape, name='negative_input')
anchor_embedding = embedding_model(anchor_input)
positive_embedding = embedding_model(positive_input)
negative_embedding = embedding_model(negative_input)
inputs = [anchor_input, positive_input, negative_input]
outputs = [anchor_embedding, positive_embedding, negative_embedding]
triplet_model = Model(inputs, outputs)
triplet_model.add_loss(K.mean(triplet_loss(outputs)))
return embedding_model, triplet_model
And this is how I'm trying to run the training:
if __name__ == '__main__':
data = pd.read_csv(path_csv)
train, test = train_test_split(data, train_size=0.7, random_state=1337)
file_id_mapping_train = {k: v for k, v in zip(train.Image.values, train.Id.values)}
file_id_mapping_test = {k: v for k, v in zip(test.Image.values, test.Id.values)}
gen_tr = gen(SampleGen(file_id_mapping_train))
gen_te = gen(SampleGen(file_id_mapping_test))
embedding_model, triplet_model = get_model()
for i, layer in enumerate(embedding_model.layers):
print(i, layer.name, layer.trainable)
for layer in embedding_model.layers[379:]:
layer.trainable = True
for layer in embedding_model.layers[:379]:
layer.trainable = False
triplet_model.compile(loss=None, optimizer=Adam(0.0001))
history = triplet_model.fit(x=gen_tr,
validation_data=gen_te,
epochs=10,
verbose=1,
steps_per_epoch=200,
validation_steps=20,
callbacks=create_callbacks())
The csv contains two columns (Image, Id) and I am generating triplets on the go using a generator. The layer 379 is the last layer of the network so I just leave that as trainable. I let it run for some epochs and it seems like it doesn't converge, it stays around 2.30. On epochs like 20, the loss is even higher than what I've started with. Here you can see what I mean: train example Is there anything wrong with the way I think about the problem?
Thank you!
I am using tensorflow 2.0 and trying to make a actor critic algorithm to play the game of cartpole. I have done everything right but getting the following error: ValueError: No gradients provided for any variable: ['dense/kernel:0', 'dense/bias:0', 'dense_1/kernel:0', 'dense_1/bias:0'].
Please help me out
Here is my code:
import gym
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
MAX_EPISODES = 2000
GAMMA = 0.9
LR_A = 0.001
LR_C = 0.01
env = gym.make("CartPole-v0")
N_ACTIONS = env.action_space.n
N_FEATURES = 4
def make_actor(n_features, n_actions):
inputs = tf.keras.Input(shape=[n_features])
hidden = tf.keras.layers.Dense(20, activation=tf.nn.relu)(inputs)
dist = tf.keras.layers.Dense(n_actions, activation=tf.nn.softmax)(hidden)
model = tf.keras.Model(inputs=inputs, outputs=dist)
return model
def make_critic(n_features):
inputs = tf.keras.Input(shape=[n_features])
hidden = tf.keras.layers.Dense(20, activation=tf.nn.relu)(inputs)
value = tf.keras.layers.Dense(1)(hidden)
model = tf.keras.Model(inputs=inputs, outputs=value)
return model
actor = make_actor(N_FEATURES, N_ACTIONS)
critic = make_critic(N_FEATURES)
actor.summary()
critic.summary()
actor_optimizer = tf.keras.optimizers.Adam(LR_A)
critic_optimizer = tf.keras.optimizers.Adam(LR_C)
def loss_actor(s, a, td_error):
dist = actor(s.reshape(1, 4)).numpy()
log_prob = np.log(dist[0, a])
exp_v = np.mean(log_prob * td_error)
return tf.multiply(exp_v, -1)
def loss_critic(s, s_, r, gamma):
s, s_ = s[np.newaxis, :], s_[np.newaxis, :]
v = critic(s)
v_ = critic(s_)
td_error = r + gamma * v_ - v
return tf.multiply(td_error, 1)
def train(max_episodes):
for episode in range(max_episodes):
s = env.reset().astype(np.float32)
t = 0
track_r = []
while True:
dist = actor(s.reshape(1, 4)).numpy()
a = np.random.choice(range(N_ACTIONS), p=dist.ravel())
s_, r, done, info = env.step(a)
s_ = s_.astype(np.float32)
if done: r=-20
track_r.append(r)
with tf.GradientTape() as cri_tape, tf.GradientTape() as act_tape:
td_error = loss_critic(s, s_, r, GAMMA)
gradient = cri_tape.gradient(td_error, critic.trainable_variables)
critic_optimizer.apply_gradients(zip(gradient,critic.trainable_variables))
with tf.GradientTape() as act_tape:
neg_exp_v = loss_actor(s, a, td_error.numpy())
gradient = act_tape.gradient(neg_exp_v, critic.trainable_variables)
actor_optimizer.apply_gradients(zip(gradient, actor.trainable_variables))
s = s_
t += 1
if done:
print("Episode:{} Steps:{}".format(episode+1, t))
train(MAX_EPISODES)
The error is on line 69:actor_optimizer.apply_gradients(zip(gradient, actor.trainable_variables))
When I tried to print out the gradients for the actor the result was None.
I am really not getting where the problem is.
I am implementing tensorflow version of LBCNN which has the code here: https://github.com/juefeix/lbcnn.torch. The problem here is that when I try to rewrite in tensorflow, the cost function keeps high and fluctuating. It mires me 2 weeks that even I debug everything, I still don't know where am I wrong.
Torch code:
-- resnet.lua
local function createModel(opt)
local function basicblock(nChIn,nChOut,sz)
local s = nn.Sequential()
local shareConv = Convolution(nChIn,nChOut,sz,sz,1,
1,(sz-1)/2,(sz-1)/2)
s:add(SBatchNorm(nChIn))
s:add(shareConv)
s:add(ReLU())
s:add(Convolution(nChOut,nChIn,1,1))
local identity = nn.Identity()
local output= nn.Sequential(): add(nn.ConcatTable()
:add(s):add(identity)):add(nn.CAddTable(true))
return output
end
local sz = opt.convSize
local nInputPlane = opt.nInputPlane
local nChIn = opt.numChannels
local nChOut = opt.numWeights
-- define model to train
model = nn.Sequential()
model:add(Convolution(nInputPlane,nChIn,sz,sz,1,1,1,1))
model:add(SBatchNorm(nChIn))
model:add(ReLU(true))
for stages = 1,opt.depth do
model:add(basicblock(nChIn,nChOut,sz))
end
model:add(Avg(5,5,5,5))
-- stage 3 : standard 2-layer neural network
model:add(nn.Reshape(nChIn*opt.view))
model:add(nn.Dropout(0.5))
model:add(nn.Linear(nChIn*opt.view,
math.max(opt.nClasses,opt.full)))
model:add(cudnn.ReLU())
model:add(nn.Dropout(0.5))
model: add(nn.Linear (math.max(opt.full,opt.nClasses), opt.nClasses))
model:cuda()
return model
end
return createModel
Tensorflow code:
def cnn(prev_input, lbc_size, lbc_channels, output_channels):
shortcut = tf.identity(prev_input)
B = tf.contrib.layers.batch_norm(prev_input)
Z = tf.contrib.layers.conv2d(inputs = B, num_outputs = lbc_channels,
kernel_size = 3, stride = 1, padding = "SAME", activation_fn = None)
A1 = tf.nn.relu(Z)
A2 = tf.contrib.layers.conv2d(inputs = A1,
num_outputs=output_channels,
kernel_size = 1, stride = 1,
padding = "SAME", activation_fn = None)
A3 = tf.add(A2, shortcut)
return A3
def model(X, Keep_probability):
with tf.name_scope("Pre-Conv"):
X1 = tf.contrib.layers.conv2d(inputs = X, num_outputs =
output_channels,kernel_size = lbc_size, stride = 1,
padding = "SAME", activation_fn = None)
X2 = tf.contrib.layers.batch_norm(X1)
X3 = tf.nn.relu(X2)
X_in = X3
for i in range(conv_layers):
with tf.name_scope("conv"):
X_out,BB,ZZ,AA,AAA = cnn(X_in, lbc_size, lbc_channels,
out_channels)
X_in = X_out
with tf.name_scope("AvgPool"):
Z = tf.nn.avg_pool(value = X_in, ksize = [1, 5, 5, 1],
strides = [1, 5, 5, 1], padding = "VALID")
with tf.name_scope("Flatter"):
P = tf.contrib.layers.flatten(Z)
with tf.name_scope("Dropout"):
F1 = tf.nn.dropout(x = P, keep_prob = 0.5)
with tf.name_scope("Fully"):
F2 = tf.contrib.layers.fully_connected(inputs = F1,
num_outputs = fc_hidden_units, activation_fn = tf.nn.relu)
with tf.name_scope("Dropout"):
F3 = tf.nn.dropout(x = F2, keep_prob = 0.5)
with tf.name_scope("Fully"):
F4 = tf.contrib.layers.fully_connected(inputs = F3,
num_outputs = output_classes, activation_fn = None)
return F4
Assume all the parameters, I passed correctly. I just want to ask that whether 2 architecture is the same or not? One more thing is their code using SGD with momentum and weight_decay while I use AdamOptimizer, is this make the difference? Thank you so much.
I did not look over your entire code to check that indeed it's the same, but usually there is a difference in examples to convergence as you change the optimizer from momentum to adam. You should also need to retune hyperparameters to get good performance.
I am a beginner in tensorflow and I am trying to train a model using "mini batch". To do that I created a generator and iterate it. The problem I encounter is that, at the beginning of the epoch, the train seems fast (many batch per seconds) then the train slow down (1 batch per second) so I am wondering where I am wrong in my code but I do not find the problem.
def prepare_data(filename):
'''load file which give path and label for the data'''
f = open(filename, 'r')
data = [line.split() for line in f]
feat =[]
label=[]
for l in data:
feat.append(l[0])
label.append(l[1])
n_samples = len(feat)
shuf = list(range(n_samples))
random.shuffle(shuf)
count = Counter(label)
print(count)
feature = [feat[i] for i in shuf]
label = np.array(label, dtype=np.int)
return feature, label[shuf]
def get_specgrams(paths, nsamples=16000):
'''
Given list of paths, return specgrams.
'''
# read the wav files
wavs = [wavfile.read(x)[1] for x in paths]
# zero pad the shorter samples and cut off the long ones.
data = []
for wav in wavs:
if wav.size < 16000:
d = np.pad(wav, (nsamples - wav.size, 0), mode='constant')
else:
d = wav[0:nsamples]
data.append(d)
# get the specgram
#specgram = [signal.spectrogram(d, nperseg=256, noverlap=128)[2] for d in data]
#specgram = [s.reshape(129, 124, -1) for s in specgram]
return np.asarray(data)
def get_specgram(path, nsamples=16000):
'''
Given path, return specgrams.
'''
# read the wav files
wav = wavfile.read(path)[1]
# zero pad the shorter samples and cut off the long ones.
if wav.size < 16000:
d = np.pad(wav, (nsamples - wav.size, 0), mode='constant')
else:
d = wav[0:nsamples]
# get the specgram
#specgram = [signal.spectrogram(d, nperseg=256, noverlap=128)[2] for d in data]
#specgram = [s.reshape(129, 124, -1) for s in specgram]
return d
# multci classification binary labels
def one_hot_encode(labels, n_unique_labels=31):
n_labels = len(labels)
#print('number of unique labels:', n_unique_labels)
one_hot_encode = np.zeros((n_labels,n_unique_labels))
one_hot_encode[np.arange(n_labels), labels] = 1
return np.array(one_hot_encode, dtype=np.int)
#create_path_file('train/audio/')
def model(tr_features, tr_labels, ts_features, ts_labels):
# remove gpu device error
config = tf.ConfigProto(allow_soft_placement = True)
# parameters
BATCH_SIZE = 4
number_loop = math.ceil(len(tr_features)/BATCH_SIZE)
training_epochs = 10
n_dim = 16000
n_classes = 31 #len(np.unique(ts_labels))
n_hidden_units_one = 280
n_hidden_units_two = 300
sd = 1 / np.sqrt(n_dim)
learning_rate = 0.1
# get test data
ts_features, ts_labels = get_data(ts_features, ts_labels)
# Model
X = tf.placeholder(tf.float32,[None,n_dim])
Y = tf.placeholder(tf.float32,[None,n_classes])
W_1 = tf.Variable(tf.random_normal([n_dim,n_hidden_units_one], mean = 0, stddev=sd))
b_1 = tf.Variable(tf.random_normal([n_hidden_units_one], mean = 0, stddev=sd))
h_1 = tf.nn.tanh(tf.matmul(X,W_1) + b_1)
W_2 = tf.Variable(tf.random_normal([n_hidden_units_one,n_hidden_units_two], mean = 0, stddev=sd))
b_2 = tf.Variable(tf.random_normal([n_hidden_units_two], mean = 0, stddev=sd))
h_2 = tf.nn.sigmoid(tf.matmul(h_1,W_2) + b_2)
W = tf.Variable(tf.random_normal([n_hidden_units_two,n_classes], mean = 0, stddev=sd))
b = tf.Variable(tf.random_normal([n_classes], mean = 0, stddev=sd))
y_ = tf.nn.softmax(tf.matmul(h_2,W) + b)
init = tf.initialize_all_variables()
# function and optimizers
cost_function = -tf.reduce_sum(Y * tf.log(y_))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)
correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# train loop
cost_history = np.empty(shape=[1],dtype=float)
y_true, y_pred = None, None
with tf.Session(config = config) as sess:
sess.run(init)
for epoch in range(training_epochs):
print(' ## Epoch n°', epoch+1 )
batch = batch_generator(BATCH_SIZE, tr_features, tr_labels)
acc_total = 0.0
for cpt, (train_features_batch, train_labels_batch) in enumerate(batch):
_,cost = sess.run([optimizer,cost_function],feed_dict={X:train_features_batch,Y:train_labels_batch})
cost_history = np.append(cost_history,cost)
correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
acc = accuracy.eval(feed_dict={X:train_features_batch,Y:train_labels_batch})
acc_total = (acc_total*cpt + acc)/(cpt+1)
print('Train accuracy : ', acc_total, '[',str(cpt+1), '/',str(number_loop), ']' ,flush=True, end='\r')
clear_output()
print('Train accuracy : ', acc_total)
y_pred = sess.run(tf.argmax(y_,1),feed_dict={X: ts_features})
y_true = sess.run(tf.argmax(ts_labels,1))
print('Test accuracy: ', round(sess.run(accuracy, feed_dict={X: ts_features, Y: ts_labels}) , 3))
fig = plt.figure(figsize=(10,8))
plt.plot(cost_history)
plt.axis([0,training_epochs,0,np.max(cost_history)])
plt.show()
p,r,f,s = precision_recall_fscore_support(y_true, y_pred, average='micro')
print("F-Score:", round(f,3))
def batch_generator(batch_size, feat_path, labels):
n_sample = len(feat_path)
ite = math.ceil(n_sample/batch_size)
for i in range(0, ite):
if i == ite-1:
label = one_hot_encode(labels[-batch_size:])
feat = get_specgrams(feat_path[-batch_size:])
yield (feat, label)
else:
label = one_hot_encode(labels[i*batch_size:i*batch_size+batch_size])
feat = get_specgrams(feat_path[i*batch_size:i*batch_size+batch_size])
yield (feat, label)
def get_data(feat_path, labels):
feat = get_specgrams(feat_path)
label = one_hot_encode(labels)
return feat, label
def __main__():
print('## Load data and shuffle')
feat_path, labels = prepare_data('data_labelised2.txt')
idx = int(len(labels)*0.8)
print("## Create Model")
model(feat_path[0:idx], labels[0:idx], feat_path[idx+1:], labels[idx+1:])
with tf.device('/gpu:0'):
__main__()