File size: 1,566 Bytes
c602f24 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
# -*- coding: utf-8 -*-
"""chatgpt-alphabet.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1BFy2tYkYPr7mev8Gg9AMT8ZD_Nj0hH3U
"""
# !pip install tensorflow
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Embedding
alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# Convert alphabet to integers
char_to_int = dict((c, i) for i, c in enumerate(alphabet))
int_to_char = dict((i, c) for i, c in enumerate(alphabet))
# Prepare dataset
seq_length = 1
dataX = []
dataY = []
for i in range(0, len(alphabet) - seq_length, 1):
seq_in = alphabet[i:i + seq_length]
seq_out = alphabet[i + seq_length]
dataX.append([char_to_int[char] for char in seq_in])
dataY.append(char_to_int[seq_out])
X = np.reshape(dataX, (len(dataX), seq_length, 1))
y = tf.keras.utils.to_categorical(dataY)
model = Sequential()
model.add(LSTM(32, input_shape=(X.shape[1], X.shape[2])))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
model.fit(X, y, epochs=500, batch_size=1, verbose=2)
for pattern in dataX:
x = np.reshape(pattern, (1, len(pattern), 1))
prediction = model.predict(x, verbose=0)
index = np.argmax(prediction)
result = int_to_char[index]
seq_in = [int_to_char[value] for value in pattern]
print(seq_in, "->", result)
model.save('alphabet_model.h5')
from google.colab import files
files.download('alphabet_model.h5')
|