tflearn

I am trying DNN- RNN on a text data set. It is a simple dummy data and I think the code can be used with most of text data. However I am getting error when I am trying to load the trained model and then retrain it. Please tell me If I am doing it wrong. def convert_docs(documents,no_class=2,MAX_DOCUMENT_LENGTH=200): '''Takes list of docs and associated clas list as input. Prepares it for the tflearn library. documents should be a list of strings and clas should be a numbered list of classes encoded into 0,1,2 etc. no_classes is the number of classes that are going to be used in the model this

I'm trying to expand the tflearn example for linear regression by increasing the number of columns to 21. from trafficdata import X,Y import tflearn print(X.shape) #(1054, 21) print(Y.shape) #(1054,) # Linear Regression graph input_ = tflearn.input_data(shape=[None,21]) linear = tflearn.single_unit(input_) regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square', metric='R2', learning_rate=0.01) m = tflearn.DNN(regression) m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False) print("\nRegression result:") print("Y = " + str(m.get_weights(linear.W)) + "*X + " +

This is probably a very basic question... But how do I convert checkpoint files into a single .pb file. My goal is to serve the model using probably C++ These are the files that I'm trying to convert. As a side note I'm using tflearn with tensorflow. Edit 1: I found an article that explains how to do this: https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc The problem is that I'm stuck with the following error KeyError: "The name 'Adam' refers to an Operation not in the graph." How do I fix this? Edit 2: Maybe this will shed some light on the

I have a very simple dataset for binary classification in csv file which looks like this: "feature1","feature2","label" 1,0,1 0,1,0 ... where the "label" column indicates class (1 is positive, 0 is negative). The number of features is actually pretty big but it doesn't matter for that question. Here is how I read the data: train = pandas.read_csv(TRAINING_FILE) y_train, X_train = train['label'], train[['feature1', 'feature2']].fillna(0) test = pandas.read_csv(TEST_FILE) y_test, X_test = test['label'], test[['feature1', 'feature2']].fillna(0) I want to run tensorflow.contrib.learn

My problem statement is as follows : " Object Detection and Localization using Tensorflow and convolutional neural network " What i did ? I am done with the cat detection from images using tflearn library.I successfully trained a model using 25000 images of cats and its working fine with good accuracy. Current Result : What i wanted to do? If my image consist of two or more than two objects in the same image for example cat and dog together so my result should be 'cat and dog' and apart from this i have to find the exact location of these two objects on the image( bounding box ) I came across