lettergram

#pm_view {
  min-width:70%;
}
#conversation-list-columns{
  border-right:none;
  background:#F7F6F6;
  max-width:30%;
}
#conversation-view{
  min-width:68%;

lettergram

def import_embedding(embedding_name="data/default"):
  
    if not embedding_name:
        return None, None

    file_flag = os.path.isfile(embedding_name+"_word_encoding.json")
    file_flag &= os.path.isfile(embedding_name+"_cat_encoding.json")

    if not file_flag:
        return None, None

lettergram

if save_model_flag:
  
  # Add optimization method, loss function and optimization value
  model.compile(loss='categorical_crossentropy',
                optimizer='adam', metrics=['accuracy'])

  # "Fit the model" (train model), using training data (80% of datset)
  model.fit(x_train, y_train, batch_size=batch_size,
            epochs=epochs, validation_data=(x_test, y_test))

lettergram

max_words, batch_size, maxlen, epochs, ngram_range = 10000, 32, 500, 5, 2

# Determine the number of categories + default(i.e. sentence types)
num_classes = np.max(y_train) + 1

# Vectorize the output sentence type classifcations to Keras readable format
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

if ngram_range > 1:

lettergram

max_words, batch_size, maxlen, epochs, ngram_range = 10000, 32, 500, 5, 2

# Determine the number of categories + default(i.e. sentence types)
num_classes = np.max(y_train) + 1

# Vectorize the output sentence type classifcations to Keras readable format
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

if ngram_range > 1:

lettergram

"""
Taken from: https://github.com/keras-team/keras
Based on Joulin et al's paper:                                                                     
Bags of Tricks for Efficient Text Classification                                              
https://arxiv.org/abs/1607.01759   
"""

def create_ngram_set(input_list, ngram_value=2):
    """
    Extract a set of n-grams from a list of integers.

lettergram

import keras
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding,GlobalAveragePooling1D

model = Sequential()

# Created Embedding (Input) Layer (max_words) --> Pooling Layer
model.add(Embedding(max_words, embedding_dims, input_length=maxlen))

lettergram

max_words, batch_size, maxlen, epochs = 10000, 64, 500, 2
embedding_dims, filters, kernel_size, hidden_dims = 50, 250, 5, 150

# Determine the number of categories + default(i.e. sentence types)
num_classes = np.max(y_train) + 1

# Vectorize the output sentence type classifcations to Keras readable format
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

lettergram

import keras
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D

model = Sequential()

# Created Embedding (Input) Layer (max_words) --> Convolutional Layer
model.add(Embedding(max_words, embedding_dims, input_length=maxlen))

lettergram

max_words, batch_size, maxlen, epochs = 10000, 125, 500, 5

# Determine the number of categories + default(i.e. sentence types)
num_classes = np.max(y_train) + 1

# Vectorize the output sentence type classifcations to Keras readable format
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# Pad the input vectors to ensure a consistent length