In [31]:
import numpy as np
import os
import sys
import h5py
import datetime
import json
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
from keras.callbacks import ModelCheckpoint
from embeddings import Embeddings
In [35]:
model_name = 'POS_LSTM_' + '_1024_1024_' + loss_function + "_" + activation + "_" + str(window_size) + "_" + str(batch_size) #MODEL_NAME #POS-LSTM
In [12]:
word_embedding_dimension = 100
word_embedding_window_size = 4
batch_size = BATCH_SIZE # 32, 64, 128
epochs = EPOCH_SIZE # 10, 15, 30
window_size = WINDOW_SIZE # 3, 4, 5
accuracy_threshold = 0.85
activation = ACTIVATION_FUNCTION # sigmoid, relu, softmax
custom_accuracy = 0
loss_function = LOSS_FUNCTION # mse
In [3]:
embeddings = Embeddings(word_embedding_dimension, word_embedding_window_size, 1, 4)
tokenized_pos_sentences = embeddings.get_pos_categorical_indexed_sentences()
pos2index, index2pos = embeddings.get_pos_vocabulary()
no_of_unique_tags = len(pos2index)
In [4]:
seq_in = []
seq_out = []
# generating dataset
for sentence in tokenized_pos_sentences:
for i in range(len(sentence)-window_size-1):
x = sentence[i:i + window_size]
y = sentence[i + window_size]
seq_in.append(x)
seq_out.append(y)
# converting seq_in and seq_out into numpy array
seq_in = np.array(seq_in)
seq_out = np.array(seq_out)
n_samples = len(seq_in)
print ("Number of samples : ", n_samples)
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x_data = seq_in
y_data = seq_out
In [24]:
# Changes to the model to be done here
model = Sequential()
model.add(LSTM(512, input_shape=(x_data.shape[1], x_data.shape[2]), return_sequences=True))
#model.add(Dropout(0.2))
model.add(LSTM(512))
#model.add(Dropout(0.2))
model.add(Dense(no_of_unique_tags, activation='relu'))
model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])
model.summary()
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model_weights_path = "../weights/"+ model_name
if not os.path.exists(model_weights_path):
os.makedirs(model_weights_path)
checkpoint_path = model_weights_path + '/pos_weights.{epoch:02d}-{val_acc:.2f}.hdf5'
checkpoint = ModelCheckpoint(filepath=checkpoint_path, monitor='val_acc', verbose=1, save_best_only=False, mode='max')
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model_fit_summary = model.fit(x_data, y_data, epochs=epochs, batch_size=batch_size, verbose=1, validation_split=0.25, callbacks=[checkpoint])
In [29]:
check_ori = 0
check_pre = 0
counter = 0
test_start = 0
test_end = 10000
list_for_hist_words = []
list_for_hist_index = []
list_for_hist_words_ori = []
list_for_hist_index_ori = []
for i in range(test_start, test_end):
test_no = i
to_predict = x_data[test_no:test_no+1]
y_ans = model.predict(to_predict)
for word, corr_int in pos2index.items():
if corr_int == np.argmax(y_ans):
#print ("pridicted: ",word, corr_int)
check_pre = corr_int
list_for_hist_words.append(word)
list_for_hist_index.append(corr_int)
if corr_int == np.argmax(y_data[test_no:test_no+1]):
#print ("original: ",word, corr_int)
check_ori = corr_int
list_for_hist_words_ori.append(word)
list_for_hist_index_ori.append(corr_int)
if check_ori == check_pre :
counter += 1
#print('\n')
print("Correct predictions: ",counter, '\nTotal Predictions: ',test_end - test_start)
custom_accuracy = counter/(test_end-test_start)
In [32]:
model_results = model_fit_summary.history
model_results.update(model_fit_summary.params)
model_results["word_embedding_dimension"] = word_embedding_dimension
model_results["word_embedding_window_size"] = word_embedding_window_size
model_results["window_size"] = window_size
model_results["batch_size"] = batch_size
model_results["epochs"] = epochs
model_results["model_name"] = model_name
model_results["accuracy_threshold"] = accuracy_threshold
model_results["activation"] = activation
model_results["custom_accuracy"] = custom_accuracy
model_results["loss_function"] = loss_function
model_results["layers"] = []
model_results["dropouts"] = []
for layer in model.layers:
if hasattr(layer, "units"):
layer_summary = {}
layer_summary["units"] = layer.get_config()["units"]
layer_summary["name"] = layer.name
model_results["layers"].append(layer_summary)
if hasattr(layer, "rate"):
dropout_summary = {}
dropout_summary["rate"] = layer.get_config()["rate"]
model_results["dropouts"].append(dropout_summary)
text_file_path = "../weights/{0}/model_results.json".format(model_name)
with open(text_file_path, "w") as f:
json.dump(model_results, f)
In [15]:
# import matplotlib.pyplot as plt
# %matplotlib inline
In [16]:
# pos_vocab = [v for (k,v) in index2pos.items()]
In [17]:
# plt.figure(figsize=(16,5))
# plt.hist(list_for_hist_index, width=1, color='r', alpha=0.5)
# plt.hist(list_for_hist_index_ori, width=1, color='b', alpha=0.5)
# plt.xticks(range(len(pos_vocab)),pos_vocab, rotation='vertical')
# plt.show()
In [18]:
# list_x = []
# list_y = []
# data_all = []
# for i in range(0,1500):
# list_x.append((index2pos[np.argmax(x_data[i][0])], index2pos[np.argmax(x_data[i][1])]))
# list_y.append(index2pos[np.argmax(y_data[i])])
# data_all.append((str(list_x[i]),list_y[i]))
In [19]:
# from nltk import ConditionalFreqDist as cfd
# from nltk.collocations import *
# import plotly.offline as plot
# import plotly.graph_objs as go
# plot.offline.init_notebook_mode(connected=True)
# import pandas as pd
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# cfd_res = cfd(data_all)
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# df = pd.DataFrame(cfd_res).fillna(0)
# mat = df.as_matrix()
# #mat
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# trace = go.Heatmap(z = mat,
# x=df.columns,
# y=list(df.index))
# data=[trace]
# plot.iplot(data, filename='labelled-heatmap')
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