Created
June 14, 2019 19:38
-
-
Save fumiakiy/cc9107e2f4b66dbc0a20ee1eabc04dc1 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import os | |
import re | |
import sklearn | |
import numpy as np | |
import tensorflow as tf | |
def main(): | |
path = '/tmp/normalized/' | |
data = [] | |
for i in range(51): | |
data.append([]) | |
labels = [] | |
for f in os.listdir(path): | |
m = re.match(r'(\w)_\d\.txt', f) | |
if (m == None): | |
continue | |
char = m.group(1) | |
with open(os.path.join(path + f), 'r') as lines: | |
i = 0 | |
for line in lines: | |
data[i].append(int(line.rstrip())) | |
i += 1 | |
labels.append(0 if char == 'a' else 1 if char == 'b' else 2) | |
# construct the ml stuff | |
features = {} | |
feature_columns = [] | |
for i in range(len(data)): | |
key = str(i) | |
features[key] = np.array(data[i]) | |
feature_columns.append(tf.feature_column.numeric_column(key=key)) | |
# Build 2 hidden layer DNN with 10, 10 units respectively. | |
classifier = tf.estimator.DNNClassifier( | |
feature_columns=feature_columns, | |
# Two hidden layers of 10 nodes each. | |
hidden_units=[10, 10], | |
# The model must choose between 3 classes. | |
n_classes=3) | |
#classifier = tf.estimator.LinearClassifier( | |
# feature_columns=feature_columns, | |
# # The model must choose between 3 classes. | |
# n_classes=3) | |
batch_size = 100 | |
train_steps = 1000 | |
# Train the Model. | |
classifier.train( | |
input_fn=lambda:train_input_fn(features, np.array(labels), | |
batch_size), | |
steps=train_steps) | |
# Evaluate the model. | |
eval_result = classifier.evaluate( | |
input_fn=lambda:eval_input_fn(features, np.array(labels), | |
batch_size)) | |
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result)) | |
test_data = [] | |
test_labels = ['2', '0', '1'] | |
for i in range(51): | |
test_data.append([]) | |
with open(os.path.join(path + 'c.txt'), 'r') as lines: | |
i = 0 | |
for line in lines: | |
test_data[i].append(int(line.rstrip())) | |
i += 1 | |
with open(os.path.join(path + 'a.txt'), 'r') as lines: | |
i = 0 | |
for line in lines: | |
test_data[i].append(int(line.rstrip())) | |
i += 1 | |
with open(os.path.join(path + 'b.txt'), 'r') as lines: | |
i = 0 | |
for line in lines: | |
test_data[i].append(int(line.rstrip())) | |
i += 1 | |
test_features = {} | |
for i in range(len(test_data)): | |
test_features[str(i)] = np.array(test_data[i]) | |
predictions = classifier.predict( | |
input_fn=lambda:eval_input_fn(test_features, | |
labels=None, | |
batch_size=batch_size)) | |
template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"') | |
for pred_dict, expec in zip(predictions, test_labels): | |
class_id = pred_dict['class_ids'][0] | |
probability = pred_dict['probabilities'][class_id] | |
print(template.format(class_id, | |
100 * probability, expec)) | |
def train_input_fn(features, labels, batch_size): | |
"""An input function for training""" | |
# Convert the inputs to a Dataset. | |
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels)) | |
# Shuffle, repeat, and batch the examples. | |
#dataset = dataset.shuffle(1000).repeat().batch(batch_size) | |
dataset = dataset.repeat().batch(batch_size) | |
# Return the dataset. | |
return dataset | |
def eval_input_fn(features, labels, batch_size): | |
"""An input function for evaluation or prediction""" | |
features=dict(features) | |
if labels is None: | |
# No labels, use only features. | |
inputs = features | |
else: | |
inputs = (features, labels) | |
# Convert the inputs to a Dataset. | |
dataset = tf.data.Dataset.from_tensor_slices(inputs) | |
# Batch the examples | |
assert batch_size is not None, "batch_size must not be None" | |
dataset = dataset.batch(batch_size) | |
# Return the dataset. | |
return dataset | |
main() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment