Neural Network loss drastically jumps during training - tensorflow

Question

During training of my LSTM network the loss/acc went from 0.75/85% to 4.97/17% from the 42 epoch to the 44 epoch.
Why would this happen?
I only have 1500 training examples currently and am overfitting heavily. Would this be a cause?
For context I am using keras and a lstm network to predict reactions to text on slack. My training data are label encoded sentences and I am predicting the reaction class which is just a one hot representation of all the possible classes.
Here is my model in Keras
# create the model
embedding_vecor_length = 128
model = Sequential()
model.add(Embedding(max_words, embedding_vecor_length, input_length=max_message_length))
model.add(LSTM(1024))
model.add(Dense(classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
The loss and accuracy end up recovering by the 100th epoch. Is this something to worry about?
# coding: utf-8
# In[1]:
import pandas as pd
import re
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
import os
pd.options.display.max_columns = 999
pd.options.display.max_colwidth = 999
np.random.seed(7)
# In[2]:
raw_data = pd.DataFrame()
for file in os.listdir('data/random'):
temp_df = pd.read_json(path_or_buf='data/random/' + file, orient='values', dtype=False)
raw_data = pd.concat([raw_data, temp_df])
for file in os.listdir('data/company-wide'):
temp_df = pd.read_json(path_or_buf='data/company-wide/' + file, orient='values', dtype=False)
raw_data = pd.concat([raw_data, temp_df])
for file in os.listdir('data/politics'):
temp_df = pd.read_json(path_or_buf='data/politics/' + file, orient='values', dtype=False)
raw_data = pd.concat([raw_data, temp_df])
# In[3]:
raw_data.shape
# In[4]:
# Only selected messages with reactions
data = raw_data.loc[(raw_data['reactions'].isnull() == False) & (raw_data['text'] != '')][['reactions', 'text']]
# In[5]:
data.shape
# In[6]:
def extractEmojiName(x):
max_count = 0
result = ''
for emoji in x:
if (emoji['count'] > max_count):
result = emoji['name']
return result
def removeUrls(x):
line = re.sub(r"<(http|https).*>", "", x)
return line
def removeUsername(x):
line = re.sub(r"<#.*>", "", x)
return line
# In[7]:
data['reactions_parsed'] = data['reactions'].apply(lambda x: extractEmojiName(x))
# In[8]:
data['text'] = data['text'].apply(lambda x: removeUrls(x))
data['text'] = data['text'].apply(lambda x: removeUsername(x))
# In[9]:
max_words = 10000
tokenizer = Tokenizer(nb_words=max_words)
tokenizer.fit_on_texts(data['text'])
text_vectors = tokenizer.texts_to_sequences(data['text'])
data['text_vector'] = text_vectors
# In[10]:
encoder = LabelEncoder()
data['reactions_encoded'] = encoder.fit_transform(data['reactions_parsed'])
# In[11]:
data
# In[12]:
classes = len(data['reactions_parsed'].unique())
target_vector = data['reactions_encoded'].values
reactions_vector = np.eye(classes)[target_vector]
data['reactions_vector'] = reactions_vector.tolist()
# In[13]:
max_message_length = data['text_vector'].apply(lambda x: len(x)).max()
# In[14]:
X_train, X_test, y_train, y_test = train_test_split(text_vectors, reactions_vector, test_size=.2, stratify=reactions_vector)
# In[15]:
X_train = np.array(X_train)
X_test = np.array(X_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
# In[17]:
X_train = sequence.pad_sequences(X_train, maxlen=max_message_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_message_length)
# In[18]:
# create the model
embedding_vecor_length = 128
model = Sequential()
model.add(Embedding(max_words, embedding_vecor_length, input_length=max_message_length))
model.add(Dropout(0.2))
model.add(LSTM(1024))
model.add(Dropout(0.2))
model.add(Dense(classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
# In[ ]:
model.fit(X_train, y_train, nb_epoch=35, batch_size=64)
# In[ ]:
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=1)
print("Accuracy: %.2f%%" % (scores[1]*100))
# In[45]:
scores
# In[56]:
def show_predictions(model, X_test, y_test):
predictions = model.predict(X_test)
index = 0
for prediction in predictions:
print('Prediction -> ' + encoder.inverse_transform(prediction.argmax()))
print('Actual -> ' + encoder.inverse_transform(y_test[index].argmax()))
index+=1
# In[57]:
show_predictions(model, X_test, y_test)
# In[58]:
show_predictions(model, X_train[0:100], y_train[0:100])
# In[ ]: