91 lines
2.5 KiB
Python
91 lines
2.5 KiB
Python
|
import fire
|
||
|
|
import ray
|
||
|
|
import pandas as pd
|
||
|
|
import tensorflow as tf
|
||
|
|
import numpy as np
|
||
|
|
from tensorflow import keras
|
||
|
|
from utils import *
|
||
|
|
from model import Model
|
||
|
|
from constants import *
|
||
|
|
|
||
|
|
CHECKPOINT = 'checkpoints/temp.h5'
|
||
|
|
|
||
|
|
SEED = 1
|
||
|
|
np.random.seed(SEED)
|
||
|
|
df = pd.read_pickle('data.p')
|
||
|
|
|
||
|
|
dataset_size, x_columns, y_columns, dataset = dataframe_to_dataset_temp_precip(df)
|
||
|
|
batch_size = 5
|
||
|
|
epochs = 500
|
||
|
|
|
||
|
|
def baseline_model():
|
||
|
|
model = keras.models.Sequential()
|
||
|
|
params = {
|
||
|
|
'kernel_initializer': 'lecun_uniform',
|
||
|
|
'bias_initializer': 'zeros',
|
||
|
|
}
|
||
|
|
model.add(keras.layers.Dense(x_columns, input_dim=x_columns, **params, activation='elu'))
|
||
|
|
model.add(keras.layers.Dense(6, **params, activation='relu'))
|
||
|
|
model.add(keras.layers.Dense(y_columns, **params))
|
||
|
|
|
||
|
|
model.compile(loss='mse', optimizer='adam', metrics=['mae'])
|
||
|
|
return model
|
||
|
|
|
||
|
|
model = baseline_model()
|
||
|
|
model.summary()
|
||
|
|
|
||
|
|
dataset = dataset.shuffle(500)
|
||
|
|
TRAIN_SIZE = int(dataset_size * 0.85)
|
||
|
|
TEST_SIZE = dataset_size - TRAIN_SIZE
|
||
|
|
(training, test) = (dataset.take(TRAIN_SIZE),
|
||
|
|
dataset.skip(TRAIN_SIZE))
|
||
|
|
training_batched = training.batch(batch_size).repeat()
|
||
|
|
test_batched = test.batch(batch_size).repeat()
|
||
|
|
|
||
|
|
logger.debug('Model dataset info: size=%s, train=%s, test=%s', dataset_size, TRAIN_SIZE, TEST_SIZE)
|
||
|
|
|
||
|
|
# model.load_weights(CHECKPOINT)
|
||
|
|
|
||
|
|
def predict():
|
||
|
|
columns = INPUTS
|
||
|
|
|
||
|
|
YEAR = 2000
|
||
|
|
|
||
|
|
print(columns)
|
||
|
|
print(df[0:batch_size])
|
||
|
|
inputs = df[columns].to_numpy()
|
||
|
|
inputs = normalize_ndarray(inputs, df[columns].to_numpy())
|
||
|
|
print(inputs[0:batch_size])
|
||
|
|
|
||
|
|
out_columns = []
|
||
|
|
for season in SEASONS:
|
||
|
|
out_columns += ['temp_{}_{}'.format(season, YEAR), 'precip_{}_{}'.format(season, YEAR)]
|
||
|
|
|
||
|
|
print(out_columns)
|
||
|
|
|
||
|
|
out = model.predict(inputs)
|
||
|
|
print(out)
|
||
|
|
print(df[out_columns][0:batch_size])
|
||
|
|
print(denormalize(out, df[out_columns].to_numpy()))
|
||
|
|
|
||
|
|
def train():
|
||
|
|
tfb_callback = tf.keras.callbacks.TensorBoard(batch_size=batch_size, log_dir='temp_logs')
|
||
|
|
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=CHECKPOINT, monitor='val_loss')
|
||
|
|
|
||
|
|
model.fit(training_batched,
|
||
|
|
batch_size=batch_size,
|
||
|
|
epochs=epochs,
|
||
|
|
steps_per_epoch=int(TRAIN_SIZE / batch_size),
|
||
|
|
validation_data=test_batched,
|
||
|
|
validation_steps=int(TEST_SIZE / batch_size),
|
||
|
|
callbacks=[tfb_callback, checkpoint_callback],
|
||
|
|
verbose=1)
|
||
|
|
|
||
|
|
model.save_weights(CHECKPOINT)
|
||
|
|
|
||
|
|
# train()
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
fire.Fire({ 'predict': predict, 'train': train })
|