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from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Dense, Embedding, Flatten, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential
from tqdm import tqdm
import numpy as np
import csv
dataset = "dataset.csv"
inp_len = 32
X = []
y = []
with open(dataset, 'r') as f:
csv_reader = csv.reader(f)
for row in tqdm(csv_reader):
if row == []: continue
label = int(row[0])
text = row[1]
text = [ord(char) for char in text]
X.append(text)
y.append(label)
X = np.array(pad_sequences(X, maxlen=inp_len, padding='post'))
y = np.array(y)
model = Sequential()
model.add(Embedding(input_dim=1500, output_dim=128, input_length=inp_len))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(512, activation="tanh"))
model.add(Dropout(0.5))
model.add(Dense(200, activation="selu"))
model.add(Dense(128, activation="softplus"))
model.add(Dense(1, activation="softplus"))
model.compile(optimizer=Adam(learning_rate=0.00001), loss="mse", metrics=["accuracy",])
model.fit(X, y, epochs=2, batch_size=4, workers=4, use_multiprocessing=True)
model.save("net.h5")
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