Deep Neural Network Library
Project description
It is for eliminating repeat jobs of machine learning. Also it can makes your code more beautifully and Pythonic.
Building Deep Neural Network
mydnn.py,
import dnn
import tensorflow as tf
class MyDNN (dnn.DNN):
n_seq_len = 24
n_channels = 1024
n_output = 8
def make_place_holders (self):
# should be defined as self.x and self.y
self.x = tf.placeholder ("float", [None, self.n_seq_len, self.n_channels])
self.y = tf.placeholder ("float", [None, self.n_output])
def make_logit (self):
# building neural network with convolution 1d, rnn and dense layers
layer = self.conv1d (self.x, 2048, activation = tf.nn.relu)
layer = self.avg_pool1d (layer)
outputs = self.lstm_with_dropout (
layer, 2048, lstm_layers = 2, activation = tf.tanh
)
# hidden dense layers
layer = self.dense (outputs [-1], 1024)
layer = self.batch_norm_with_dropout (layer, self.nn.relu)
layer = self.dense (layer, 256)
layer = self.batch_norm_with_dropout (layer, self.nn.relu)
# finally, my logit
return self.dense (layer, self.n_output)
def make_label (self):
# prediction method
return tf.argmax (self.logit, 1)
def make_cost (self):
return tf.reduce_mean (tf.nn.softmax_cross_entropy_with_logits (
logits = self.logit, labels = self.y
))
def make_optimizer (self):
return tf.train.AdamOptimizer (self.learning_rate).minimize (
self.cost, global_step = self.global_step
)
def calculate_accuracy (self):
correct_prediction = tf.equal (tf.argmax(self.y, 1), tf.argmax(self.logit, 1))
return tf.reduce_mean (tf.cast (correct_prediction, "float"))
Sometimes it is very annoying to calculate complex accuracy with tensors, then can replace with calculate_complex_accuracy for calculating with numpy, python math and loop statement.
import dnn
import numpy as np
class MyDNN (dnn.DNN):
# can get additional arguments for calculating accuracy as you need
def calculate_accuracy (self, logit, y, *args, **karg):
return np.mean ((np.argmax (logit, 1) == np.argmax (y, 1)))
Training
Import mydnn.py,
import mydnn, mydataset
from tqdm import tqdm
from dnn import split
net = mydnn.MyDNN (gpu_usage = 0.4)
net.set_train_dir ('./checkpoint')
xs, ys = mydataset.load ()
train_xs, test_xs, train_ys, test_ys = split.split (xs, ys, test_size = 10000)
net.trainable (
start_learning_rate=0.0001,
decay_step=500, decay_rate=0.99,
overfit_threshold = 0.1, # stop learining if cost moving average is over threshold and keep 100 epoches continously
accuracy_thres_hold = 0.5 # save checkpoint only if accuracy is over 0.5
)
# should be behind trainable ()
net.net.set_tensorboard_dir (cf.TFBOARD_DIR) ("./logs")
net.make_writers ('Param', 'Train', 'Valid')
minibatches = split.minibatch (train_xs, train_ys, 128)
Now, we can start learning.
for epoch in tqdm (range (1000)): # 1000 epoch
# training ---------------------------------
batch_xs, batch_ys = next (minibatches)
_, lr = net.run (
net.train_op, net.learning_rate,
x = batch_xs, y = batch_ys,
dropout_rate = 0.5,
is_training = True
)
net.write_summary ('Param', {"Learning Rate": lr})
# train loss ------------------------------
logit, cost, accuracy = net.run (
net.logit, net.cost, net.accuracy,
x = train_xs, y = train_ys,
dropout_rate = 0.0,
is_training = True
)
net.write_summary ('Train', {"Accuracy": accuracy, "Cost": cost})
# valid loss -------------------------------
logit, cost, accuracy = net.run (
net.logit, net.cost, net.accuracy,
x = test_xs, y = test_ys,
dropout_rate = 0.0,
is_training = False
)
net.write_summary ('Valid', {"Accuracy": accuracy, "Cost": cost})
# check overfit if cost movement average is over overfit_threshold
if net.is_overfit ():
break
But dnn give some shortcut methods for more simpler way:
for epoch in tqdm (range (1000)): # 1000 epoch
# training ---------------------------------
batch_xs, batch_ys = next (minibatches)
lr = net.fit (batch_xs, batch_ys, dropout_rate = 0.5)
net.write_summary ('Param', {"Learning Rate": lr})
# train loss ------------------------------
r = net.train (train_xs, train_ys)
net.write_summary ('Train', {"Accuracy": r.accuracy, "Cost": r.cost})
# valid loss -------------------------------
r = net.valid (test_xs, test_ys)
net.write_summary ('Valid', {"Accuracy": r.accuracy, "Cost": r.cost})
if net.is_overfit ():
break
If you use custom accuracy calculating like this,
def calculate_accuracy (self, logit, y, debug = False):
return np.mean ((np.argmax (logit, 1) == np.argmax (y, 1)))
Then you call just update ()
# evaluate first
r = net.train (batch_xs, batch_ys)
# update r.accuracy with your accuracy function
r.update (debug = True)
net.write_summary ('Valid', {"Accuracy": r.accuracy, "Cost": r.cost})
Data Normalization
Data normalization and standardization,
train_xs = net.normalize (train_xs, normalize = True, standardize = True)
To show cumulative sum of explained_variance_ratio_ of sklearn PCA.
train_xs = net.normalize (train_xs, normalize = True, standardize = True, pca_k = -1)
Then you can decide n_components for PCA.
train_xs = net.normalize (train_xs, normalize = True, standardize = True, axis = 0, pca_k = 500)
Test dataset will be nomalized by factors of train dataset.
test_xs = net.normalize (test_xs)
This parameters will be pickled at your train directory named as normfactors. You can use this pickled file for serving your model.
Multi Model Training
You can train complete seperated models at same time.
Not like Multi Task Training in this case models share the part of training data and there’re no shared layers between models - for example, model A is a logistic regression and B is a calssification problem.
Anyway, it provides some benefits for model, dataset and code management rather than handles as two complete seperated models.
First of all, you give name to each models for saving checkpoint or tensorboard logging.
import mydnn
import dnn
net1 = mydnn.ModelA (0.3, name = 'my_model_A')
net2 = mydnn.ModelB (0.2, name = 'my_model_B')
Your checkpoint, tensorflow log and export pathes will remaped seperately to each model names like this:
checkpoint/my_model_A
checkpoint/my_model_B
logs/my_model_A
logs/my_model_B
export/my_model_A
export/my_model_B
Next, y should be concated. Assume ModelA use first 4, and ModelB use last 3.
# y length is 7
y = [0.5, 4.3, 5.6, 9.4, 0, 1, 0]
Then combine models into MultiDNN.
net = dnn.MultiDNN (net1, 4, net2, 3)
And rest of code is very same as a single DNN case.
If you need exclude data from specific model, you can use exclusion filter function.
def exclude (ys, xs = None):
nxs, nys = [], []
for i, y in enumerate (ys):
if np.sum (y) > 0:
nys.append (y)
if xs is not None:
nxs.append (xs [i])
return np.array (nys), np.array (nxs)
net1.set_filter (exclude)
Export Model
For serving model,
import mydnn
net = mydnn.MyDNN ()
net.restore ('./checkpoint')
version = net.export (
'./export',
'predict_something',
inputs = {'x': net.x},
outputs={'label': net.label, 'logit': net.logit}
)
print ("version {} has been exported".format (version))
You can serve the expoted model with TensorFlow Serving or tfserver.
Note: If you use net.normalize (train_xs), normalizing factors (mean, std, max and etc) willl be pickled and saved to model directory with tensorflow model. If you can use this file for normalizing new x data at real service.
def normalize (x):
norm_file = os.path.join (model_dir, "normfactors")
with open (norm_file, "rb") as f:
mean, std, min_, gap, normalize, standardize = pickle.load (f)
if normalize: # -1 to 1
x = -1 + 2 * ((x - min_) / gap) # gap = (max - min)
if standardize:
x = (x - mean) / std
return x
Helpers
There’re several helper modules.
Generic DNN Model Helper
from dnn import costs, predutil
Data Processing Helper
from dnn import split, vector
import dnn.video
import dnn.audio
import dnn.image
import dnn.text
dnn Class Methods & Properties
You can override or add anything. If it looks good, contribute to this project please.
Predefined Operations & Creating
You should or could create these operations by overriding methods,
train_op: create with ‘make_optimizer’
logit: create with ‘DNN.make_logit’
cost: create with ‘DNN.make_cost’
accuracy: create with ‘DNN.calculate_accuracy’
label (optional): create with ‘DNN.make_label’, determine your label index(es) or something from your logit
Predefined Place Holders
x
y
dropout_rate: if negative value, dropout rate will be selected randomly.
is_training
n_sample: Numner of x (or y) set. This value will be fed automatically, do not feed.
Layering
dense
batch_norm
batch_norm_with_dropout
lstm
lstm_with_dropout
dropout
full_connect
conv1d
conv2d
conv3d
max_pool1d
max_pool2d
max_pool3d
avg_pool1d
avg_pool2d
avg_pool3d
sequencial_connect
Optimizers
You can use predefined optimizers.
def make_optimizer (self):
return self.optimizer ("adam")
# Or
return self.optimizer ("rmsprob", mometum = 0.01)
Available optimizer names are,
“adam”
“rmsprob”
“momentum”
“clip”
“grad”
“adagrad”
“adagradDA”
“adadelta”
“ftrl”
“proxadagrad”
“proxgrad”
see dnn/optimizers.py
Training
fit
train
valid
trainable
run
get_epoch: equivalant with DNN.eval (self.global_step)
is_overfit
normalize
l1
l2
Model
save
restore
export
reset_dir
set_train_dir
eval
Tensor Board
set_tensorboard_dir
make_writers
write_summary
History
0.1: project initialized
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