I'm trying to create a neural network that can learn how to write text character by character from the book David Copperfield (via Project Gutenburg).
It starts great, then forgets punctuation around epoch 25 and devolves into nonsense at epoch 26. I've been trying to find a starting point where I can start attacking this problem. I've read research papers on the concept of clipping the gradient to stop it from vanishing or exploding, but I'm having a hard time finding a way to first visualize the gradient and what's going wrong and then how to clip it at the most appropriate value.
I've saved checkpoint models from all fifty epochs.
I've tried using a clipping the gradient at 5 based on a research paper about gradient clipping in LSTMs and it didn't change anything. I don't have to budget to find the optimum value through experimentation, but if that's the only way to do it I'll make it work.
I've been working on this a long time, but I'm self-taught and feel a a bit out of my depth here. A nudge in the right direction from a subject matter expert would be greatly appreciated.
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Code Follows: (I started with Tensorflow, but switched to TFLearn for simplicity. I'm willing to learn any frameworks that have the tools to solve the problem, though. I'm a self-taught student, so learning is really the only objective here.)
import time start_script_time = time.time() import numpy as np import tflearn import random import pickle ''' create data ''' log_file = 'dickens_log.txt' def my_log(text, filename=log_file): text = str(text) print(text) with open(filename, 'a', newline='\n') as file: file.write(text + '\n') try: book_name = 'as_loaded.txt' book = open(book_name, errors='ignore', encoding='ascii', newline='\n').read() except: book_name = 'copperfield.txt' book = open(book_name, errors='ignore', encoding='utf-8', newline='\n').read() #book = book.replace('\r', '') #book = book.replace('\n', ' ') with open('as_loaded.txt', 'w', newline='\n') as file: file.write(book) # make smaller slice for quickly testing code on CPU # book = book[0:1500] # del(book_name) # length of strings in the training set string_length = 30 def process_book(book, string_length, redundant_step=3): # Remember to pickle to dictionary as a binary. This is pretty critical for loading your model on a different machine than you trained on. try: pickle_ld = open('charDict.pi', 'rb') charDict = pickle.load(pickle_ld) pickle_ld.close() except: # dictionary of character-number pairs chars = sorted(list(set(book))) charDict = dict((c, i) for i, c in enumerate(chars)) #charDict.pop('\r') pickle_sv = open('charDict.pi', 'wb') pickle.dump(charDict, pickle_sv) pickle_sv.close() len_chars = len(charDict) # train is a string input and target is the # expected next character train =  target =  for i in range(0, len(book)-string_length, redundant_step): train.append(book[i:i+string_length]) target.append(book[i+string_length]) # create containers for data with appropriate dimensions # 3D (n_samples, sample_size, n_categories) X = np.zeros((len(train), string_length, len_chars), dtype=np.bool) # 2D (n_samples, n_categories) y = np.zeros((len(train), len_chars), dtype=np.bool) # fill arrays for i, string in enumerate(train): for j, char in enumerate(string): # X is a sparse 3D tensor where a 1 value signals # that a information is present in 3rd dimension index X[i, j, charDict[char]] = 1 y[i, charDict[target[i]]] = 1 return charDict, X, y charDict, X, y = process_book(book, string_length) ''' build the network ''' # number of hidden layers in each LSTM layer lstm_hidden = 512 drop_rate = 0.5 net = tflearn.input_data(shape=(None, string_length, len(charDict))) # input shape is the length of the strings by the number of characters # leading None is necessary if no placeholders net = tflearn.lstm(net, lstm_hidden, return_seq=True) net = tflearn.dropout(net, drop_rate) # You have to use a separate dropout layer. There's a glitch where tflean # will drop out all the time, not just during training, making prediction # impossible. net = tflearn.lstm(net, lstm_hidden, return_seq=True) net = tflearn.dropout(net, drop_rate) net = tflearn.lstm(net, lstm_hidden, return_seq=False) net = tflearn.dropout(net, drop_rate) net = tflearn.fully_connected(net, len(charDict), activation='softmax') net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.005) # https://www.quora.com/What-is-gradient-clipping-and-why-is-it-necessary model = tflearn.SequenceGenerator(net, dictionary=charDict, seq_maxlen=string_length, clip_gradients=5, checkpoint_path='model_checkpoint_v3') my_log('Character dictionary for ' + book_name) my_log(charDict) my_log('charDict length: ' + str(len(charDict))) my_log('&&&&&&&&&&&&&&&&&') def random_seed_test(book, temp=0.5, gen_length=300): my_log('#######################') seed_no = random.randint(0, len(book) - string_length) seed = book[seed_no : seed_no + string_length] my_log('(temp ' + str(temp) + ') ' + 'Seed: "' + seed + '"') my_log('++++++++++++++++++++++') my_log(model.generate(seq_length=gen_length, temperature=temp, seq_seed=seed)) my_log('#######################') # If you train one epoch at a time in a loop, you can get an idea # of how the model progressed. With other ML problems, error rate and # accuracy reveal a lot, but with this problem performance is subjective. for epoch in range(50): start_epoch = time.time() my_log('======================================================') my_log('Begin epoch %d' % (epoch+1)) model.fit(X, y, validation_set=0.1, batch_size=128, n_epoch=1) my_log('End epoch %d' % (epoch+1)) epoch_time = time.time() - start_epoch my_log('This epoch took ' + str(epoch_time) + ' seconds.') random_seed_test(book, temp=0.5, gen_length=1000) random_seed_test(book, temp=0.75, gen_length=1000) random_seed_test(book, temp=1.0, gen_length=1000) my_log('End epoch %d' % (epoch+1)) my_log('======================================================') full_time = time.time() - start_script_time my_log('This program took ' + str(full_time) + ' seconds.') model.save('dickens_compute_4.model') my_log('finished')