Created
June 14, 2019 19:51
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import os | |
import re | |
import sklearn | |
import numpy as np | |
import tensorflow as tf | |
def main(): | |
path = './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() |
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