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