epochs = 100
batch_size = 64
rnn_size = 256 # number of neurons in each layer
num_layers = 2
learning_rate = 0.005
keep_probability = 0.75
beam_width = 3
Training Hyperparameters
start = 200000
learning_rate_decay = 0.95
min_learning_rate = 0.0005
display_step = 20 # Check training loss after every 20 batches
stop_early = 0
stop = 3 # If the update loss does not decrease in 3 consecutive update checks, stop training
per_epoch = 3 # Make 3 update checks per epoch
sample_5k.zip
base_path = 'G:\AI\data\cnn\'
base_path = '..\data\cnn\'
path = base_path + 'sample_5k\'
path = base_path + 'stories\'
path = base_path + 'stories\sample\' articles_pickle_filename = "articles.pickle" headlines_pickle_filename = "headlines.pickle" model_pickle_filename = "model.pickle" word_embedding_matrix_filename = "word_embedding_matrix.pickle"
''' https://fasttext.cc/docs/en/english-vectors.html '''
model_path ='G:\Python\MLLearning\MachineLearning\data\wiki-news-300d-1M.vec'
model_path= 'C:\Temp\python_files\MLLearning\data\wiki-news-300d-1M.vec'
to avoid words that are used less that threshold value
threshold = 2
Dimension size as per pre-trained data
embedding_dim = 300 max_text_length = 1000 max_summary_length = 20 min_length = 2 unk_text_limit = 200
Set the Hyperparameters
epochs = 100 batch_size = 64 rnn_size = 256 # number of neurons in each layer num_layers = 2 learning_rate = 0.005 keep_probability = 0.75 beam_width = 3
Training Hyperparameters
start = 200000 learning_rate_decay = 0.95 min_learning_rate = 0.0005 display_step = 20 # Check training loss after every 20 batches stop_early = 0 stop = 3 # If the update loss does not decrease in 3 consecutive update checks, stop training per_epoch = 3 # Make 3 update checks per epoch