01

from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from sklearn.metrics import accuracy_score

model = Sequential()
model.add(Dense(units=32, activation='relu', input_dim=len(X_train.columns)))
model.add(Dense(units=64, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='sgd', metrics='accuracy')

model.fit(X_train, y_train, epochs=200, batch_size=32)

pred = model.predict(X_test)
pred = np.where(
  pred > 0.5,
  1,
  0
)

model.save('tfmodel')
del model
model = load_model('tfmodel')

tf_lite_converter = tf.lite.TFLiteConverter.from_keras_model(model)
tf_lite_converter.optimizations = [tf.lite.Optimize.DEFAULT]
tf_lite_converter.target_spec.supported_types = [tf.float16]
tflite_model = tf_lite_converter.convert()

tf_lite_converter  = tf.lite.TFLiteConverter.from_keras_model(model)
tf_lite_converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
tflite_model = tf_lite_converter.convert()