SimpleRNN.py

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import TimeDistributed, Dense, Dropout, SimpleRNN, RepeatVector
from tensorflow.keras.callbacks import EarlyStopping, LambdaCallback

from termcolor import colored

all_chars = "0123456789+"
num_features = len(all_chars)
print("number of features ", num_features)
char_to_index = dict((c,i) for i,c in enumerate(all_chars))
index_to_char = dict((i,c) for i,c in enumerate(all_chars))
print(index_to_char)
print(char_to_index)

def generate_data():
    first = np.random.randint(0,100)
    second = np.random.randint(0,100)
    example = str(first) + "+" + str(second)
    label = str(first+second)
    return example, label
generate_data()

hidden_units = 128
max_time_steps = 5
model  = Sequential([
    SimpleRNN(hidden_units, input_shape=(None,num_features)),
    RepeatVector(max_time_steps),
    SimpleRNN(hidden_units,return_sequences= True),
    TimeDistributed(Dense(num_features,activation='softmax'))
])
model.summary()

model.compile(
    loss = "categorical_crossentropy",
    optimizer = "adam",
    #metric = ["accuracy"]
    #metric = [tf.keras.metrics.Accuracy()]
)


def vectorize_example(example,label):
    x = np.zeros((max_time_steps, num_features))
    y = np.zeros((max_time_steps, num_features))
    
    diff_x = max_time_steps - len(example) 
    diff_y = max_time_steps - len(label)
    
    for i,c in enumerate(example):
        x[i+diff_x,char_to_index[c]] = 1
        
    for i in range(diff_x):
        x[i,char_to_index["0"]] = 1 
        
    for i,c in enumerate(label):
        y[i+diff_y,char_to_index[c]] = 1
        
    for i in range(diff_y):
        y[i,char_to_index["0"]] = 1 
        
    return x,y

e,l = generate_data()
print(e,l)
x,y = vectorize_example(e,l)
print(x.shape,y.shape)

def devectorize_example(example):
    result = [index_to_char[np.argmax(vec)] for i,vec in enumerate(example)] 
    return ''.join(result)
devectorize_example(x)

devectorize_example(y)

def create_dataset(num_examples=2000):
    x = np.zeros((num_examples, max_time_steps, num_features))
    y = np.zeros((num_examples, max_time_steps, num_features))
    for i in range(num_examples):
        e,l = generate_data()
        e_v,l_v = vectorize_example(e,l)
        x[i] = e_v
        y[i] = l_v
    return x,y
x,y = create_dataset()
print(x.shape, y.shape)

devectorize_example(x[0])
devectorize_example(y[0])

l_cb = LambdaCallback(
    on_epoch_end = lambda e,l: print('{:.2f}'.format(l['val_acc']),end = ' _ ')
)
es_cb = EarlyStopping(monitor='val_loss', patience=10)
model.fit(x,y,epochs=500,batch_size=256, validation_split=0.2,
         verbose=False, callbacks=[es_cb,l_cb])

x_test, y_test = create_dataset(10)
preds = model.predict(x_test)

for i,pred in enumerate(preds):
    y= devectorize_example(y_test[i])
    y_hat = devectorize_example(pred)
    col = "green"
    if y!= y_hat:
        col="red"
    out = "Input: " + devectorize_example(x_test[i]) + "Ouptut: " + y +"Predicted: " + y_hat
    print(colored(out,col))