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feat(nn): first model for predicting temp and precip

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This commit is contained in:
Mahdi Dibaiee 2019-02-20 09:06:03 +03:30
parent c490f2006b
commit 0d9a0068b1
2 changed files with 41 additions and 10 deletions
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47
nn.py
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@ -16,14 +16,45 @@ tf.enable_eager_execution()
df = pd.read_pickle('data_final.p')
# print(df.head())
# dataset = dataframe_to_dataset_biomes(df)
dataset = dataframe_to_dataset_temp_precip(df)
BATCH_SIZE = 15
SHUFFLE_BUFFER_SIZE = 100
LEARNING_RATE = 0.001
i = 0
for feature, target in dataset:
i += 1
if i > 10:
break
print('{} => {}'.format(feature, target))
# dataset = dataframe_to_dataset_biomes(df)
dataset_size, features, dataset = dataframe_to_dataset_temp_precip(df)
print(dataset_size)
dataset = dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE).repeat()
TRAIN_SIZE = dataset_size * 0.85
TEST_SIZE = dataset_size - TRAIN_SIZE
(training, test) = (dataset.take(TRAIN_SIZE), dataset.skip(TRAIN_SIZE))
print(training.make_one_shot_iterator().get_next())
model = keras.Sequential([
keras.layers.Dense(32, activation=tf.nn.relu, input_shape=[features]),
keras.layers.Dense(32, activation=tf.nn.relu),
keras.layers.Dense(2)
])
optimizer = tf.train.AdamOptimizer(LEARNING_RATE)
model.compile(loss='mse',
optimizer=optimizer,
metrics=['mae'])
model.summary()
EPOCHS = 1000
history = model.fit(
training,
epochs=EPOCHS,
verbose=1,
steps_per_epoch=int(dataset_size / BATCH_SIZE)
)
# i = 0
# for feature, target in dataset:
# print('{} => {}'.format(feature, target))
print(tf.__version__)

4
utils.py
View File

@ -51,7 +51,7 @@ def dataframe_to_dataset_biomes(df):
tf_inputs = tf.cast(normalize_ndarray(tf_inputs), tf.float32)
tf_output = tf.cast(normalize_ndarray(tf_output), tf.int32)
return tf.data.Dataset.from_tensor_slices((tf_inputs, tf_output))
return int(tf_inputs.shape[0]), 5, tf.data.Dataset.from_tensor_slices((tf_inputs, tf_output))
def dataframe_to_dataset_temp_precip(df):
rows = df.shape[0]
@ -81,5 +81,5 @@ def dataframe_to_dataset_temp_precip(df):
tf_inputs = tf.cast(normalize_ndarray(tf_inputs), tf.float32)
tf_output = tf.cast(normalize_ndarray(tf_output), tf.float32)
return tf.data.Dataset.from_tensor_slices((tf_inputs, tf_output))
return int(tf_inputs.shape[0]), 5, tf.data.Dataset.from_tensor_slices((tf_inputs, tf_output))