张量流的简单分类

我有以下问题:

• 我有一个覆盆子皮机器人,它使用四个传感器(左,前,右,后)。
• 机器人可以做以下动作:向前跑,向左拐,向右拐,向后退。
• 根据传感器的数据,我将“训练”机器人做这些动作。

所以训练机器人的基本输入如下:

• 如果传感器数据=[2,1,0,1]=>向左移动
• 如果传感器数据=[4,0,1,1]=>向左移动
• 如果传感器数据=[0,2,0,0]=>向前移动
• 如果传感器数据=[0,0,0,1]=>向后移动
• ……在这里输入更多数据..

现在训练结束后,机器人应该可以预测下一步的动作,比如:

如果传感器数据=[3、3、2、1]=>执行预测移动。

我的第一个想法是 张量流 为了解决这个问题,我无法找到实现这种(简单的)预测算法的最佳方法,因为大多数教程都是关于图像和语音识别的。

如果有人能用python向我展示一个简短的例子,说明如何使用tensorflow来实现这一点,那就太好了。

所以主要的问题是:

我如何实现一个算法,它以一个包含四个值的数组列表作为输入,然后预测给定的输出(一个变量可以有四个状态)。

解决方案: 我设法找到了一个解决方案(它使用带有tensorflow和pandas的python 3):

import tensorflow as tf
import pandas as pd

# Path to the directory where model data should be saved.
MODEL_PATH = "model"
# Path to the training data file.
TRAIN_DATA_PATH = "movement_training_data.csv"

# The csv column names
CSV_COLUMN_NAMES = ['Front', 'Back', 'Left', 'Right', 'Move']
# The moves (results) of the estimation
MOVES = ['Forward', 'Back', 'Left', 'Right']

# Defines the batch size of data taken for each training step.
batch_size = 100

# Defines how many training steps should be done.
# Weights and biases wll be adjusted after each step.
train_steps = 1000


def main(argv):
    # Reads the csv data and assigns column names. The first line is the header line.
    train_data = pd.read_csv(TRAIN_DATA_PATH, names=CSV_COLUMN_NAMES, header=0)

    # Generates a train_features and a train_label data frame.
    train_features, train_labels = train_data, train_data.pop('Move')

    # Add feature columns (all numeric).
    feature_columns = []
    for key in train_features.keys():
        feature_columns.append(tf.feature_column.numeric_column(key=key))

    # Create classifier for a deep neural network (DNN)
    classifier = tf.estimator.DNNClassifier(
            # Set the model directory.
            model_dir=MODEL_PATH,
            # Set the feature columns.
            feature_columns=feature_columns,
            # Two hidden layers of 10 nodes each.
            hidden_units=[10, 10],
            # The model must choose between 5 classes (which in this case consist of one label each).
            n_classes=4)

    # Train the Model.
    classifier.train(
        input_fn=lambda: train_input(train_features, train_labels),
        steps=train_steps)

    # Test prediction data.
    data_to_predict = {
        'Front': [115, 42, 30, 21],
        'Back': [142, 151, 120, 121],
        'Left': [145, 23, 81, 15],
        'Right': [155, 25, 43, 192],
    }

    predictions = classifier.predict(
        input_fn=lambda: eval_input(data_to_predict, labels=None))

    for prediction_dict in predictions:
        # 0 = Forward, 1 = Back, 2 = Left, 3 = Right
        class_id = prediction_dict['class_ids'][0]
        probability = prediction_dict['probabilities'][class_id]

        print(str(class_id) + ": " + str(probability))


def train_input(features, labels):
    # Convert the inputs to a data set.
    ds = tf.data.Dataset.from_tensor_slices((dict(features), labels))

    # Shuffle, repeat, and batch the examples.
    ds = ds.shuffle(1000).repeat().batch(batch_size)

    # Return the data set.
    return ds


def eval_input(features, labels):
    features = dict(features)

    if labels is None:
        # No labels, use only features.
        inputs = features
    else:
        inputs = (features, labels)

    # Convert the inputs to a data set.
    ds = tf.data.Dataset.from_tensor_slices(inputs)

    # Batch the examples
    ds = ds.batch(batch_size)

    # Return the data set.
    return ds


# Execute TensorFlow program if started directly from script
if __name__ == '__main__':
    tf.logging.set_verbosity(tf.logging.INFO)
    tf.app.run(main)

csv看起来像

Front,Back,Left,Right,Move
100,100,100,100,0
150,150,150,150,0
100,200,100,200,0
110,110,200,200,0
200,100,200,100,0
140,150,200,140,0
120,120,120,170,0
140,170,170,120,0
170,150,130,140,0
190,190,100,130,0
110,150,160,110,0
160,170,110,100,0
170,140,160,110,0
180,160,110,120,0
130,200,110,190,0
120,150,160,110,0
160,180,120,100,0
170,140,140,110,0
180,110,110,120,0
110,200,140,190,0
10,100,10,10,1
40,150,40,40,1
10,200,10,20,1
20,110,20,20,1
10,100,20,10,1
10,150,20,40,1
20,120,10,10,1
30,170,40,20,1
40,150,30,40,1
40,190,30,30,1
30,150,40,10,1
10,170,30,40,1
20,140,20,10,1
30,160,20,20,1
20,200,10,40,1
10,150,40,10,1
20,120,30,40,1
20,120,20,20,1
30,160,20,10,1
10,100,10,10,1
10,100,100,10,2
40,150,140,40,2
10,200,160,20,2
20,110,120,20,2
10,100,120,10,2
10,150,180,40,2
20,120,110,10,2
30,170,140,20,2
40,150,130,40,2
40,190,130,30,2
30,150,140,10,2
10,170,150,40,2
20,140,120,10,2
30,160,120,20,2
20,200,170,40,2
10,160,50,20,2
40,100,70,40,2
20,160,60,10,2
20,100,90,20,2
10,100,10,10,3
40,150,40,100,3
10,200,30,120,3
20,110,20,120,3
10,100,20,110,3
10,150,20,140,3
20,120,10,110,3
30,170,40,120,3
40,150,30,140,3
40,190,30,130,3
30,150,40,110,3
10,170,50,140,3
20,140,20,110,3
30,160,20,120,3
20,200,40,140,3
30,150,40,70,3
10,150,40,60,3
10,140,10,90,3
30,140,30,80,3
20,200,40,70,3