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Deep Neural Networks 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 = 3

  def make_place_holders (self):
      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

      # 1d convolution (-1, 24, 1024) => (-1, 12, 2048)
      conv = self.conv1d (self.x, 2048, activation = tf.nn.relu)

      # rnn
      output = self.lstm (
        conv, 4096, lstm_layers = 2, activation = tf.tanh
      )

      # hidden dense layers
      layer = self.dense_with_dropout (output [-1], 1024, self.nn.relu)
      layer = self.dense_with_dropout (layer, 256, 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 self.optimizer ("adam")

  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 as you need calculate accuracy
  def calculate_complex_accuracy (self, logit, y, *args, **karg):
      return np.mean ((np.argmax (logit, 1) == np.argmax (y, 1)))

Training

Import mydnn.py,

import mydnn
from tqdm import tqdm

net = mydnn.MyDNN (gpu_usage = 0.4)
net.reset_dir ('./checkpoint')
net.trainable (
  start_learning_rate=0.0001,
  decay_step=500, decay_rate=0.99,
  overfit_threshold = 0.1
)
net.reset_tensor_board ("./logs")
net.make_writers ('Param', 'Train', 'Valid')

train_minibatches = split.minibatch (train_xs, train_ys, 128)
valid_minibatches = split.minibatch (test_xs, test_ys, 128)

for epoch in tqdm (range (1000)): # 1000 epoch
  # training ---------------------------------
  batch_xs, batch_ys = next (train_minibatches)
  _, lr = net.run (
    net.optimizer, net.learning_rate,
    x = batch_xs, y = batch_ys, dropout_rate = 0.5
  )
  net.write_summary ('Param', {"Learning Rate": lr})

  # train loss ------------------------------
  cost, logit = net.run (s
    net.cost, net.logit,
    x = batch_xs, y = batch_ys, dropout_rate = 0.0
  )
  acc = net.calculate_complex_accuracy (logit, batch_ys)
  net.write_summary ('Train', {"Accuracy": acc, "Cost": cost})

  # valid loss -------------------------------
  vaild_xs, vaild_ys = next (valid_minibatches)
  cost, logit = net.run (
    net.cost, net.logit,
    x = vaild_xs, y = vaild_ys, dropout_rate = 0.0
  )
  acc = net.calculate_complex_accuracy (logit, vaild_ys)
  net.write_summary ('Valid', {"Accuracy": acc, "Cost": cost})

  # check overfit or save checkpoint if cost is the new lowest cost.
  if net.is_overfit (cost, './checkpoint'):
      break

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))

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

History

  • 0.1: project initialized

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