61 lines
1.4 KiB
Python
61 lines
1.4 KiB
Python
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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from utils import *
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tf.enable_eager_execution()
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df = pd.read_pickle('data_final.p')
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# print(df.head())
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BATCH_SIZE = 15
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SHUFFLE_BUFFER_SIZE = 100
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LEARNING_RATE = 0.001
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# dataset = dataframe_to_dataset_biomes(df)
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dataset_size, features, dataset = dataframe_to_dataset_temp_precip(df)
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print(dataset_size)
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dataset = dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE).repeat()
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TRAIN_SIZE = dataset_size * 0.85
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TEST_SIZE = dataset_size - TRAIN_SIZE
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(training, test) = (dataset.take(TRAIN_SIZE), dataset.skip(TRAIN_SIZE))
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print(training.make_one_shot_iterator().get_next())
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model = keras.Sequential([
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keras.layers.Dense(32, activation=tf.nn.relu, input_shape=[features]),
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keras.layers.Dense(32, activation=tf.nn.relu),
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keras.layers.Dense(2)
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])
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optimizer = tf.train.AdamOptimizer(LEARNING_RATE)
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model.compile(loss='mse',
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optimizer=optimizer,
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metrics=['mae'])
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model.summary()
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EPOCHS = 1000
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history = model.fit(
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training,
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epochs=EPOCHS,
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verbose=1,
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steps_per_epoch=int(dataset_size / BATCH_SIZE)
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)
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# i = 0
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# for feature, target in dataset:
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# print('{} => {}'.format(feature, target))
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print(tf.__version__)
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