import pandas as pd
import sys
import tensorflow
from tensorflow.keras import layers

X_train = pd.read_csv('train.csv')
X_test = pd.read_csv('test.csv')
X_valid = pd.read_csv('valid.csv')

Y_train = X_train.pop('stabf')
Y_train = pd.get_dummies(Y_train)

Y_test = X_test.pop('stabf')
Y_test = pd.get_dummies(Y_test)

Y_valid = X_valid.pop('stabf')
Y_valid = pd.get_dummies(Y_valid)

# model = tensorflow.keras.Sequential([
#                           layers.Input(shape=(12,)),
#                           layers.Dense(32),
#                           layers.Dense(16),
#                           layers.Dense(2, activation='softmax')
# ])

model = tensorflow.keras.Sequential()
model.add(layers.Input(shape=(12,)))
model.add(layers.Dense(32))
model.add(layers.Dense(16))
model.add(layers.Dense(2, activation='softmax'))

model.compile(
    loss=tensorflow.keras.losses.BinaryCrossentropy(),
    optimizer=tensorflow.keras.optimizers.Adam(lr=float(sys.argv[1])),
    metrics=[tensorflow.keras.metrics.BinaryAccuracy()])

history = model.fit(X_train, Y_train, epochs=2, validation_data=(X_valid, Y_valid))

model.save('grid-stability-dense.h5')