Create net.py

net.py

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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")