Build a deeper architecture! File “/afs/”, line 2476, in save_weights_to_hdf5_group Also, the model learnt to produce a sonnet-like word structure. the world with the shee the world with thee, dataY = np_utils.to_categorical(dataY) # convert the target character into one hot encoding, I’m sorry to hear that, I have some suggestions here that might help: However, i got a floating point exception (core dumped) running the code. That’s additional init overhead, but I think it might worth it. This is the most tricky part when it comes to building LSTM models. how did you decide the number of hidden units? Will update . For example now I have a problem to load the weights, using the example above on python 3 with intermediate weight files: Traceback (most recent call last): That is, ‘a’ might be assigned a lower number as compared to ‘z’, but that doesn’t signify any relationship between the two. 1 question though : One improvement suggested was add dropout to the visible input layer. Just updated all libraries. The end goal is to generate something new right? but thou, contracted to the world's false sporoe. Anyway, I will look into it. You can read from sys.stdin, for example: For user input you can use userinput = stdin.readline(), userinput = “Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to”, sort_sen = sorted(list(p)) RuntimeError: Unable to create link (Name already exists) Hello Jason, great tutorial, as always!! Yesterday I came across a really interesting paper. Total Patterns: 236984. on Epoche 13, loss is 1.5. with eyes so dond touls be thy domfornds. Sorry, I don’t follow, what is the concern exactly? Or is the corpus just too small? Perhaps progressive loading with a custom data generator would be the way to go? via an automated process and grab people’s attention! Yes, you can use an encoder-decoder model. Maybe overlearning? Now this works but it is giving gobeldegook instead of semi-english repeating words. Perhaps I need to train for hours longer and have loss <2.5? – first three numbers are connected like this: first number is the sum of second and third; Thanks. (1) I wanted to know how we can extend this example to a many-to-many model or if you could link to any articles you have on this site that covers this. (previous versions are OK, at least with 0.10.0rc0). When it converts the index value to char ie.., int_to_char[index] in the dictionary int_to_char the key for 1 is the newline. Does the reasoning for 256 come from other parameters? Contact | I am working on a similar LSTM network in tensorflow for a sequence-labeling problem, and so far it appears that my generated output sequence is always exactly the same for a fixed starting input. Can you explain why you are using 256 as your output dimension for LSTM? I would like to give my model some characteristics, key-words and that it generates me a description from it. 0. You can find the entire code on my git repo. Thanks. I’m attempting to run the lastf ull code example for generating text using the loaded LSTM model. Let me elaborate: This normalizes the integer values by the largest integer value. The “decoder” is a conditional language model. What makes a text generator more efficient is its capability to generate relevant stories. Rather, I need to find the probability of the entire segment ” I am Sam” and “I am egg” to be able to tell which one makes more sense. Discover how in my new Ebook: Also, dropout layers with a 20% dropout have been added to check for over-fitting. For a particular input, I want the model to generate corresponding output. Perhaps survey the literature to see what your options are? validation_split=0.33, shuffle=True, callbacks=callbacks) Any input is appreciated, Jason. In the next section we will look at using this model to generate new text sequences. which btw I’m running on a google cloud instance with 120 GB of RAM but it exhausts it all. Or have you ever heard about it? A special thing about this type of data is that if two events are occurring in a particular time frame, the occurrence of event A before event B is an entirely different scenario as compared to the occurrence of event A after event B. Perhaps test making predictions prior to saving and then use the same code after loading so that you know it works. But i feel like it is a good start. Instead of letters, can we map each individual word to a number? I'm Jason Brownlee PhD KeyError: “Can’t open attribute (Can’t locate attribute)”. Yes, the code will work on either backend. 64 I’m excited to try combining this with nltk… it’s probably going to be long and frustrating, but I’ll try to let you know my results. The hidden state of the RNN. prediction = model.predict(x, verbose=0) You’re making the world a better place. – a bag of word encoding (e.g. Hi Jason. seq_in is set but doesn’t seemed to be used. Made the necessary changes. AttributeError Traceback (most recent call last) What is wrong with the initial shape of list of lists? generating several similar sentences : The difference is how you handle the probabilities. Hello, I modified my code to treat the characters as actual words as my base dataset is extremely small ~200 sentences. but that doesn’t signify any relationship between the two. Is there any benchmark dataset for this task, to actually evaluate the model ? Have you tried to run the example a few times? for 20 epochs. I have one question though. char_to_int = dict((c, i) for i, c in enumerate(chars)), dataX = [] . I was wondering what you meant with “Add dropout to the visible input layer and consider tuning the dropout percentage.”, seems it appears to attack this problem. This report uses TensorFlow to build an RNN text generator and builds a high-level API in Python3. # reshape X to be [samples, time steps, features] (stored in Y). can i use the same step and same code for another language other than English? o beau aal wierefe, oo ttomnaei ofs. For example the numeric data is 37, 40, 20, and the corresponding text is ‘hot and the humidity is moderate’ can be represented as: X Y Hi Jason! It says “ValueError: You are trying to load a weight file containing 3 layers into a model with 2 layers.”. Thank you! The header is obvious and ends with the text: The footer is all of the text after the line of text that says: You should be left with a text file that has about 3,330 lines of text. The process is straightforward: It wouldn’t take long, and the Twitter account might even get a small handful followers (e.g. the iroelge gatesds bever neagne brmions I exactly implemented your code. Phrasee’s core business is using natural language generation to write marketing language (that outperforms human language). In the generated text, the sonnet number is centred, ie preceded by a number of spaces, as in the original. You might have to get creative and explore a range of ideas to see what works well/best. If iteratively, then are they learnt a) first before the other weights or b) are the embedding projection weights learnt along with the other weights. 4.26335023e-27 1.49551446e-13 6.07275735e-24 7.06751049e-01