from create_sentiment_featuresets import create_feature_sets_and_labels
import tensorflow as tf
# from tensorflow.examples.tutorials.mnist import input_data
import pickle
import numpy as np
neg = open(
r'F:\havingnewinPython\machineLearning\Sandextutorial\DeepLearning\neg.txt'
)
pos = open(
'F:\havingnewinPython\machineLearning\Sandextutorial\DeepLearning\pos.txt',
'r')
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(neg, pos)
n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500
n_classes = 2
batch_size = 100
hm_epochs = 10
x = tf.placeholder('float')
y = tf.placeholder('float')
hidden_1_layer = {
'f_fum': n_nodes_hl1,
'weight': tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl1]))
}
hidden_2_layer = {
# Youtube video: https://www.youtube.com/watch?list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v&time_continue=1&v=6rDWwL6irG0
from create_sentiment_featuresets import create_feature_sets_and_labels
import tensorflow as tf
# from tensorflow.examples.tutorials.mnist import input_data
import pickle
import numpy as np
# Data files are downloaded from:
# Positive data: https://pythonprogramming.net/static/downloads/machine-learning-data/pos.txt
# Negative data: https://pythonprogramming.net/static/downloads/machine-learning-data/neg.txt
pos_file = 'data/pos.txt'
neg_file = 'data/neg.txt'
pickle_file = 'data/sentiment_set.pickle'
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(pos_file, neg_file)
n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500
n_classes = 2
batch_size = 100
hm_epochs = 10
x = tf.placeholder('float')
y = tf.placeholder('float')
hidden_1_layer = {'f_fum': n_nodes_hl1,
'weight': tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl1]))}
from create_sentiment_featuresets import create_feature_sets_and_labels
import tensorflow as tf
# from tensorflow.examples.tutorials.mnist import input_data
import pickle
import numpy as np
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(
'/home/alon/PycharmProjects/cnn-text-classification-tf/data/rt-polaritydata/rt-polarity.pos',
'/home/alon/PycharmProjects/cnn-text-classification-tf/data/rt-polaritydata/rt-polarity.neg'
)
n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500
n_classes = 2
batch_size = 100
hm_epochs = 10
x = tf.placeholder('float')
y = tf.placeholder('float')
hidden_1_layer = {
'f_fum': n_nodes_hl1,
'weight': tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl1]))
}
hidden_2_layer = {
'f_fum': n_nodes_hl2,
'weight': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from create_sentiment_featuresets import create_feature_sets_and_labels
import numpy as np
trainX, trainY, testX, testY = create_feature_sets_and_labels(
'pos.txt', 'neg.txt')
numNodesHL1 = 1500
numNodesHL2 = 1500
numNodesHL3 = 1500
numClasses = 2
batchSize = 100
x = tf.placeholder('float')
y = tf.placeholder('float')
hiddenLayer1 = {
'f_fum': numNodesHL1,
'weight': tf.Variable(tf.random_normal([len(trainX[0]), numNodesHL1])),
'bias': tf.Variable(tf.random_normal([numNodesHL1]))
}
hiddenLayer2 = {
'f_fum': numNodesHL2,
'weight': tf.Variable(tf.random_normal([numNodesHL1, numNodesHL2])),
'bias': tf.Variable(tf.random_normal([numNodesHL2]))
}
hiddenLayer3 = {
'f_fum': numNodesHL3,
'weight': tf.Variable(tf.random_normal([numNodesHL2, numNodesHL3])),
import tensorflow as tf
from create_sentiment_featuresets import create_feature_sets_and_labels
import numpy as np
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(
"pos.txt", "neg.txt")
"""
Feed forward:
Input > weight > hiddenlayer 1 (activationfunction) > weight > hiddenlayer 2 (activation function)
> weights > output layer
Compare output to intended output > cost function (cross entropy)
Back propogation:
Optimization function (optimizer) > minimize the cost (AdamOptimizer, Stochastic gradient descent, AdaGrad)
"""
# Nodes in the hidden layers
n_nodes_hl1 = 1000
n_nodes_hl2 = 1000
n_nodes_hl3 = 1000
n_classes = 2
batch_size = 100
X = tf.placeholder('float', [None, len(train_x[0])])
# Label
y = tf.placeholder('float')
hidden_1_layer = {
'weights': tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
more likely we'd get better results.
The act of sending the data straight through our network means we're operating a feed
forward neural network. The adjustments of weights backwards is our back propagation.
We do feeding forward and back propagation however many times we want. The cycle is
called an Epoch. We can pick any number for number of epochs. After each epoch, we've
hopefully further fine-tuned our weights lowering our cost and improving accuracy.
'''
# one_hot means one eleent out of others is literally "hot" or on. This is useful for a
# in the case of sentiment prediction, it's either positive or negative
# sentiments, so we will model output as
# positive = [1,0]
# negative = [0,1]
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(
'ident_nn_pos.txt', 'ident_nn_neg.txt')
'''
in building the model, we consider the number of nodes each hidden layer will have.
Nodes in each layer need not be identical, but it can be tweaked, depending on what
we are trying to model (TBD).
Batches are used to control how many features we are going to optimize at once, as computers
are limited by memory.
'''
n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500
n_classes = 2
batch_size = 100
hm_epochs = 14
from create_sentiment_featuresets import create_feature_sets_and_labels
import tensorflow as tf
# from tensorflow.examples.tutorials.mnist import input_data
import pickle
import numpy as np
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(
'/path/to/pos.txt')
n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500
n_classes = 2
batch_size = 100
hm_epochs = 10
x = tf.placeholder('float')
y = tf.placeholder('float')
hidden_1_layer = {
'f_fum': n_nodes_hl1,
'weight': tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl1]))
}
hidden_2_layer = {
'f_fum': n_nodes_hl2,
'weight': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl2]))
}
from create_sentiment_featuresets import create_feature_sets_and_labels
import tensorflow as tf
#from tensorflow.examples.tutorials.mnist import input_data
import pickle
import numpy as np
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(
'D:\Data\Desktop\pos.txt', 'D:\Data\Desktop\pos2.txt')
n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500
n_classes = 2
batch_size = 100
hm_epochs = 10
x = tf.placeholder('float')
y = tf.placeholder('float')
hidden_1_layer = {
'f_fum': n_nodes_hl1,
'weight': tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl1]))
}
hidden_2_layer = {
'f_fum': n_nodes_hl2,
'weight': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl2]))
}
Note: This script requires tensorflow, numpy dependencies to be installed
'''
from create_sentiment_featuresets import create_feature_sets_and_labels
from RecordAudioData import recordAudioTest
from AudioToSpectrogram import getAudioData
from AudioToSpectrogram import saveSpectrogramData
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
import tensorflow as tf
import numpy as np
from numpy import argmax
import os
orginal_path = os.getcwd()
train_x, train_y, test_x, test_y, lexicon = create_feature_sets_and_labels()
n_classes = 10
batch_size = 10
hm_epochs = 10
x = tf.placeholder('float')
y = tf.placeholder('float')
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def maxpool2d(x):
# size of window movement of window
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from create_sentiment_featuresets import create_feature_sets_and_labels
import numpy as np
train_x, train_y, test_x, test_y = create_feature_sets_and_labels('/Users/surthi/gitrepos/tensorflow/ml-data/pos.txt', '/Users/surthi/gitrepos/tensorflow/ml-data/neg.txt')
num_nodes_hl1 = 500
num_nodes_hl2 = 500
num_nodes_hl3 = 500
n_classes = 2
batch_size= 100
# x matrix size = height x width (None x 784). (784 = 28x28 image)
x = tf.placeholder('float', [None, len(train_x[0])])
y = tf.placeholder('float')
def nn_model(data):
hidden_layer_1 = {'weights': tf.Variable(tf.random_normal([len(train_x[0]), num_nodes_hl1])),
'biases':tf.Variable(tf.random_normal([num_nodes_hl1]))}
hidden_layer_2 = {'weights': tf.Variable(tf.random_normal([num_nodes_hl1, num_nodes_hl2])),
'biases':tf.Variable(tf.random_normal([num_nodes_hl2]))}
hidden_layer_3 = {'weights': tf.Variable(tf.random_normal([num_nodes_hl2, num_nodes_hl3])),
'biases':tf.Variable(tf.random_normal([num_nodes_hl3]))}
output_layer = {'weights': tf.Variable(tf.random_normal([num_nodes_hl3, n_classes])),
'biases':tf.Variable(tf.random_normal([n_classes]))}
# (input_data * weights + biases)
l1 = tf.add(tf.matmul(data, hidden_layer_1['weights']), hidden_layer_1['biases'])
l1 = tf.nn.relu(l1)
l2 = tf.add(tf.matmul(l1, hidden_layer_2['weights']), hidden_layer_2['biases'])
from create_sentiment_featuresets import create_feature_sets_and_labels
import tensorflow as tf
import numpy as np
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(
'dataFiles/pos.txt', 'dataFiles/neg.txt')
n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500
n_classes = 2
batch_size = 100
hm_epochs = 10
x = tf.placeholder('float')
y = tf.placeholder('float')
hidden_1_layer = {
'f_fum': n_nodes_hl1,
'weight': tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl1]))
}
hidden_2_layer = {
'f_fum': n_nodes_hl2,
'weight': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl2]))
}
hidden_3_layer = {
from create_sentiment_featuresets import create_feature_sets_and_labels
import tensorflow as tf
# from tensorflow.examples.tutorials.mnist import input_data
import pickle
import numpy as np
train_x, train_y, test_x, test_y = create_feature_sets_and_labels('/home/alon/PycharmProjects/cnn-text-classification-tf/data/rt-polaritydata/rt-polarity.pos', '/home/alon/PycharmProjects/cnn-text-classification-tf/data/rt-polaritydata/rt-polarity.pos')
n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500
n_classes = 2
batch_size = 100
hm_epochs = 10
x = tf.placeholder('float')
y = tf.placeholder('float')
hidden_1_layer = {'f_fum': n_nodes_hl1,
'weight': tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl1]))}
hidden_2_layer = {'f_fum': n_nodes_hl2,
'weight': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl2]))}
hidden_3_layer = {'f_fum': n_nodes_hl3,
'weight': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl3]))}
import tensorflow as tf
# from tensorflow.examples.tutorials.mnist import input_data
# mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
from create_sentiment_featuresets import create_feature_sets_and_labels
import numpy as np
train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt')
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 2 # m
batch_size = 100
# height * width
x = tf.placeholder('float',[None, len(train_x[0])]) # m
y = tf.placeholder('float')
def neural_network_model(data):
# input_data * weights + biases
# m //
hidden_1_layer = {'weights':tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}
# // m
hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}
hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
import tensorflow as tf
import numpy as np
import os
from create_sentiment_featuresets import create_feature_sets_and_labels
BASE_FOLDER = os.path.abspath(os.path.dirname(__file__))
pos_path = os.path.join(BASE_FOLDER, "resources/pos.txt")
neg_path = os.path.join(BASE_FOLDER, "resources/neg.txt")
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(
pos_path, neg_path)
# 10 classes, from 0 to 9
# 0 = [1,0,0,0,0,0,0,0,0,0]
# 5 = [0,0,0,0,0,1,0,0,0,0]
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 2
batch_size = 100
# height x width
x = tf.placeholder('float', [None, len(train_x[0])])
y = tf.placeholder('float')
def neural_network_model(data):
hidden_1_layer = {
# coding: utf-8
# In[2]:
from create_sentiment_featuresets import create_feature_sets_and_labels
import tensorflow as tf
#from tensorflow.examples.tutorials.mnist import input_data
import pickle
import numpy as np
train_x,train_y,test_x,test_y = create_feature_sets_and_labels('/home/SMO/Documents/ML_Harrison_Python/pos.txt','/home/SMO/Documents/ML_Harrison_Python/neg.txt')
n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500
n_classes = 2
batch_size = 100
hm_epochs = 10
x = tf.placeholder('float')
y = tf.placeholder('float')
hidden_1_layer = {'f_fum':n_nodes_hl1,
'weight':tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}
hidden_2_layer = {'f_fum':n_nodes_hl2,
'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
import tensorflow as tf
import numpy as np
from create_sentiment_featuresets import create_feature_sets_and_labels
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(
'pos.txt', 'neg.txt')
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 2
batch_size = 100
x = tf.placeholder('float', [None, len(train_x[0])])
y = tf.placeholder('float')
def neural_network_model(data):
hidden_1_layer = {
'weights': tf.Variable(tf.random_normal([len(train_x[0]),
n_nodes_hl1])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))
}
hidden_2_layer = {
'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))
}
hidden_3_layer = {
from create_sentiment_featuresets import create_feature_sets_and_labels
import tensorflow as tf
#from tensorflow.examples.tutorials.mnist import input_data
import pickle
import numpy as np
train_x, train_y, test_x, test_y = create_feature_sets_and_labels(
'./samplefiles/positive.txt', './samplefiles/negetive.txt')
n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500
n_classes = 2
batch_size = 100
hm_epochs = 10
x = tf.placeholder('float')
y = tf.placeholder('float')
hidden_1_layer = {
'f_fum': n_nodes_hl1,
'weight': tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl1]))
}
hidden_2_layer = {
'f_fum': n_nodes_hl2,
'weight': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'bias': tf.Variable(tf.random_normal([n_nodes_hl2]))
}
