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.

Table of Contents

## 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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