import pandas as pd
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import SGD

x_file = "X.csv"
y_file = "Y.csv"

X = pd.read_csv(x_file, sep=",", header=None)
X = X.values.T
normalized_X = (X - X.mean()) / X.std()

Y = pd.read_csv(y_file, sep=",", header=None)
Y = Y.values

# %%
model = Sequential()
model.add(Dense(units=1024, activation="relu", input_dim=20530))
model.add(Dense(units=50, activation="relu"))
model.add(Dense(units=1, activation="sigmoid"))

sgd = SGD(learning_rate=0.001)
model.compile(loss="binary_crossentropy", optimizer=sgd, metrics=["accuracy"])
model.fit(normalized_X, Y, batch_size=1, epochs=50)

Y_pred = model.predict(normalized_X)
sum(Y_pred > .5)