本文介绍使用TensorFlow构建卷积神经网络解决kaggle上的digit-recognizer问题。
数据规格
kaggle提供的数据集来自MNIST上的60000条手写数字数据。数据中每个手写数字图像使用28 * 28 的灰度图表示 。
模型结构
本文展示的CNN模型包括1个输入层，2个卷积层，2个池化层，1个全连接层以及1个大小为10的输出层。卷积层使用Relu activation function引入非线性特性。池化层使用max-pooling，大小为2 * 2, 步长设置为2。输出层使用softmax activation function 输出0到1的浮点数(最后一层所有node结果相加结果为1)。
模型构建过程参照Tensorflow Tutorials
代码实现
"""
File Name: tf_cnn.py
Author: ce39906
mail: [email protected]
Created Time: 2018-10-25 10:55:56
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
tf.logging.set_verbosity(tf.logging.INFO)
def cnn_model_fn(features, labels, mode):
# input layer
input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
# convolutional layer 1
conv1 = tf.layers.conv2d(
inputs = input_layer,
filters = 32,
kernel_size = [5, 5],
padding = "same",
activation = tf.nn.relu)
# pooling layer 1
pool1 = tf.layers.max_pooling2d(
inputs = conv1,
pool_size = [2, 2],
strides = 2)
# convolutional layer 2
conv2 = tf.layers.conv2d(
inputs = pool1,
filters = 64,
kernel_size = [5, 5],
padding = "same",
activation = tf.nn.relu)
# pooling layer 2
pool2 = tf.layers.max_pooling2d(
inputs = conv2,
pool_size = [2, 2],
strides = 2)
# dense layer
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.layers.dense(
inputs = pool2_flat,
units = 1024,
activation = tf.nn.relu)
dropout = tf.layers.dropout(
inputs = dense,
rate = 0.4,
training = mode == tf.estimator.ModeKeys.TRAIN)
# logits layer
logits = tf.layers.dense(
inputs = dropout,
units = 10)
# do predict
predictions = {
"classes" : tf.argmax(input = logits, axis = 1),
"probabilities" : tf.nn.softmax(logits, name = "softmax_tensor")
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode = mode,
predictions = predictions)
# calculate loss (both TRAIN and EVAL mode)
loss = tf.losses.sparse_softmax_cross_entropy(labels = labels, logits = logits)
# configure the Trainning Op
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.001)
train_op = optimizer.minimize(
loss = loss,
global_step = tf.train.get_global_step())
return tf.estimator.EstimatorSpec(
mode = mode,
loss = loss,
train_op = train_op)
# add evaluation metrics (for EVAL mode)
eval_metric_ops = {
"accuracy" : tf.metrics.accuracy(
labels = labels, predictions = predictions["classes"])}
return tf.estimator.EstimatorSpec(
mode = mode,
loss = loss,
eval_metric_ops = eval_metric_ops)
def main():
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
labels = np.array(train_data.pop('label'))
data = StandardScaler().fit_transform(np.float32(train_data.values))
validation_size = 10000
train_data, valid_data = data[ : -validation_size], data[-validation_size : ]
train_labels, valid_labels = labels[: -validation_size], labels[-validation_size : ]
test_data = StandardScaler().fit_transform(np.float32(test_data.values))
classifier = tf.estimator.Estimator(
model_fn = cnn_model_fn,
model_dir = "/tmp/cnn_model")
# Set up logging for predictions
tensors_to_log = {"probabilities" : "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(
tensors = tensors_to_log, every_n_iter = 50)
# train the model
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x = {"x" : train_data},
y = train_labels,
batch_size = 100,
num_epochs = None,
shuffle = True)
classifier.train(
input_fn = train_input_fn,
steps = 20000,
hooks = [logging_hook])
# evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x = {"x" : valid_data},
y = valid_labels,
num_epochs = 1,
shuffle = False)
eval_results = classifier.evaluate(input_fn = eval_input_fn)
print (eval_results)
# evaluate the model and print results
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
x = {"x" : test_data},
num_epochs = 1,
shuffle = False)
preidct_results = classifier.predict(input_fn = predict_input_fn)
test_labels = []
for predict_result in preidct_results:
test_labels.append(predict_result['classes'])
test_labels = np.array(test_labels)
submission = pd.DataFrame({'ImageId' : (np.arange(test_labels.shape[0]) + 1),'Label' : test_labels})
submission.to_csv('submission.csv', index = False)
if __name__ == '__main__':
main()
结果
交叉测试准确率为97.58%
提交到kaggle后准确率为97.35%