113 lines
3.3 KiB
Python
113 lines
3.3 KiB
Python
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from __future__ import absolute_import, division, print_function
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# TensorFlow and tf.keras
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import tensorflow as tf
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from tensorflow import keras
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# Helper libraries
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import os.path
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from utils import *
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RANDOM_SEED = 1
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tf.enable_eager_execution()
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tf.set_random_seed(RANDOM_SEED)
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np.random.seed(RANDOM_SEED)
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df = pd.read_pickle('data_final.p')
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class Model():
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def __init__(self, name, batch_size=100, shuffle_buffer_size=500, learning_rate=0.001, epochs=1):
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self.name = name
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self.path = "checkpoints/{}.hdf5".format(name)
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self.batch_size = batch_size
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self.shuffle_buffer_size = shuffle_buffer_size
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self.learning_rate = learning_rate
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self.epochs = epochs
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def prepare_dataset(self, df, fn):
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self.dataset_fn = fn
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dataset_size, features, output_size, dataset = fn(df)
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self.dataset = dataset.shuffle(self.shuffle_buffer_size)
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self.TRAIN_SIZE = int(dataset_size * 0.85)
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self.TEST_SIZE = dataset_size - self.TRAIN_SIZE
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(training, test) = (self.dataset.take(self.TRAIN_SIZE).batch(self.batch_size).repeat(),
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self.dataset.skip(self.TRAIN_SIZE).batch(self.batch_size).repeat())
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self.dataset_size = dataset_size
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self.features = features
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self.output_size = output_size
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self.training = training
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self.test = test
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def create_model(self, layers):
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self.model = keras.Sequential([
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keras.layers.Dense(layers[0], activation=tf.nn.relu, input_shape=[self.features])
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] + [
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keras.layers.Dense(n, activation=tf.nn.relu) for n in layers[1:]
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] + [
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keras.layers.Dense(self.output_size)
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])
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def compile(self):
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self.model.load_weights(self.path)
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optimizer = tf.train.AdamOptimizer(self.learning_rate)
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self.model.compile(loss='mse',
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optimizer=optimizer,
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metrics=['mae', 'accuracy'])
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def evaluate(self):
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return self.model.evaluate(
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self.test,
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batch_size=self.batch_size,
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steps=int(self.dataset_size / self.batch_size),
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verbose=1
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)
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def train(self):
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self.model.summary()
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checkpoint = keras.callbacks.ModelCheckpoint(filepath, monitor='acc', verbose=1, mode='max')
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self.model.fit(
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self.training,
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batch_size=self.batch_size,
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epochs=self.epochs,
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steps_per_epoch=int(self.dataset_size / self.batch_size),
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callbacks=[checkpoint],
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verbose=1
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)
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def predict(self, a):
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return np.argmax(self.model.predict(a), axis=1)
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A = Model('a', batch_size=100, shuffle_buffer_size=500, learning_rate=0.001, epochs=2)
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B = Model('b', batch_size=100, shuffle_buffer_size=500, learning_rate=0.001, epochs=850)
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if __name__ == "__main__":
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B.prepare_dataset(df, dataframe_to_dataset_biomes)
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B.create_model([64, 128])
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B.compile()
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# for inp, out in B.test.take(1).make_one_shot_iterator():
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# print(inp, out)
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# print(np.unique(nums))
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# print(np.unique(predictions))
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print('loss: {}, evaluation: {}'.format(*B.evaluate()))
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# B.train()
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A.prepare_dataset(df, dataframe_to_dataset_temp_precip)
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A.create_model([4])
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A.compile()
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# A.train()
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