Ask Ghassem - Recent questions tagged tensorflow

Passing variable length sentences to Tensorflow LSTM

Mon, 11 Feb 2019 05:06:27 +0000

I have a tensorflow LSTM model for predicting the sentiment. I build the model with the maximum sequence length 150. (Maximum number of words) While making predictions, i have written the code as below:

batchSize = 32
maxSeqLength = 150

def getSentenceMatrix(sentence):
    arr = np.zeros([batchSize, maxSeqLength])
    sentenceMatrix = np.zeros([batchSize,maxSeqLength], dtype='int32')
    cleanedSentence = cleanSentences(sentence)
    cleanedSentence = ' '.join(cleanedSentence.split()[:150])
    split = cleanedSentence.split()
    for indexCounter,word in enumerate(split):
        try:
            sentenceMatrix[0,indexCounter] = wordsList.index(word)
        except ValueError:
            sentenceMatrix[0,indexCounter] = 399999 #Vector for unkown words
    return sentenceMatrix

input_text = "example data"
inputMatrix = getSentenceMatrix(input_text)

In the code i'm truncating my input text to 150 words and ignoring remaining data.Due to this my predictions are wrong.

cleanedSentence = ' '.join(cleanedSentence.split()[:150])

I know that if we have lesser length than sequence length we can pad with zero's. What we need to do if we have more length. Can you suggest me the best way to do this. Thanks in advance.

Training neutral net with Tensor Flow

Mon, 12 Nov 2018 20:09:54 +0000

Hello,

I have a jpeg, where each image is around 1080 x 2048 in size and I have around 3000 of those images. I want to train a simple NN on this data. However, I am not sure how to feed the image data in to tensor flow. The examples online have a minst data which is easier since the image size is smaller.

When i tried make an numpy array out of the images I have i get the memory exception.

Any help would be appreciated !

Using Tensorflow.DNNClassifier, getting Error: assertion failed: [Labels must >= 0]

Wed, 24 Oct 2018 03:12:33 +0000

Hi All,

I am writing a simple program using Tensorflow and DNNClassifier. Training Data is 9 pixel with four spectral bands, i.e. 4*9=36 featurs. And each data-point will be mapped to a class (from 1 to 7).

Last parameter, is the class label.

A line of data-point is like this:

67,75,77,62,67,79,81,62,75,87,89,71,66,79,88,63,66,79,84,63,66,79,80,59,67,84,86,68,71,84,86,64,67,81,82,64,7

But I got below Error:

InvalidArgumentError (see above for traceback): assertion failed: [Labels must >= 0] [Condition x >= 0 did not hold element-wise:] [x (dnn/head/labels:0) = ] [[3][3][3]...]

I am sure there is no datapoint which has a label less than 0. Would you please advise?

import numpy as np

import pandas as pd

import tensorflow as tf

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV, KFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import StratifiedShuffleSplit

print('** DNN Classification *******************************************************')

landsatData = pd.read_csv("./resources/landsat/lantsat.1.csv")

landsatData.describe()

X_landSatAllFeatures = landsatData.iloc[:, np.arange(36)].copy()

y_midPixelAsTarget = landsatData.iloc[:, 36].copy()

# Testing and training sentences splitting (stratified + shuffled) based on the index (sentence ID)
allFeaturesIndexes = X_landSatAllFeatures.index
targetData = y_midPixelAsTarget
sss = StratifiedShuffleSplit(n_splits=1, test_size=0.3, random_state=42)

for train_index, test_index in sss.split(allFeaturesIndexes, targetData):
    train_ind, test_ind = allFeaturesIndexes[train_index], allFeaturesIndexes[test_index]

Test_Matrix = X_landSatAllFeatures.loc[test_ind]
Test_Target_Matrix = y_midPixelAsTarget.loc[test_ind]
Train_Matrix = X_landSatAllFeatures.loc[train_ind]
Train_Target_Matrix = y_midPixelAsTarget.loc[train_ind]

scaler = StandardScaler().fit(Train_Matrix)
Train_Matrix, Test_Matrix = scaler.transform(Train_Matrix), scaler.transform(Test_Matrix)

def reset_graph(seed=42):
    tf.reset_default_graph()
    tf.set_random_seed(seed)
    np.random.seed(seed)

X_train = Train_Matrix
y_train = Train_Target_Matrix
X_test = Test_Matrix
y_test = Test_Target_Matrix

xx, yy = Train_Matrix.shape
#training phase
feature_cols = [tf.feature_column.numeric_column("X", shape=[36])]
dnn_clf = tf.estimator.DNNClassifier(hidden_units=[300,100], n_classes=8, feature_columns=feature_cols)
# dnn_clf = tf.estimator.DNNClassifier(hidden_units=[300,100], n_classes=10)


input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"X": X_train}, y=y_train, num_epochs=40, batch_size=64, shuffle=True)
dnn_clf.train(input_fn=input_fn)

#testing phase
test_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"X": X_test}, y=y_test, shuffle=False)
eval_results = dnn_clf.evaluate(input_fn=test_input_fn)
print("The prediction result is : {0:.2f}%".format(100*eval_results['accuracy']))
y_pred_iter = dnn_clf.predict(input_fn=test_input_fn)
y_pred = list(y_pred_iter)
y_pred[0]


print('**********************************************************************************')