tf_model_cnn.py

import tensorflow as tf
from sklearn import preprocessing
import pandas as pd
from sklearn.model_selection import train_test_split

dropout = 0.8
n_classes = 4
batch_size = 128
hm_epochs = 10

x = tf.placeholder('float',[None,14,32,1])
y = tf.placeholder('float')

def get_data():
    df = pd.read_csv('autonomous_arena.csv')

    # filter labels containing 'S' - stop
    df = df[df['448']!='S']
    df = df[df['448']!='s']

    X = df.drop(str(14*32), axis=1)
    y = df[str(14*32)]

    X_data = X.values.reshape(-1,448)
    X_data = X_data.reshape(X_data.shape[0], 14, 32, 1)
    X_data = X_data.astype('float32')
    X_data /= 255

    le = preprocessing.LabelEncoder()
    y = le.fit_transform(y)
    y_data = tf.contrib.keras.utils.to_categorical(y, 4)

    X_train, X_test, y_train, y_test = train_test_split(X_data,y_data, random_state=0, test_size=0.3)
    X_test, X_val, y_test, y_val = train_test_split(X_test,y_test, random_state=0, test_size=0.5)
    
    return [X_train, X_test, X_val, y_train, y_test, y_val]

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')

def maxpool2d(x):
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

def cnn_model(x):
    weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,1,32])),
               'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
               'W_fc':tf.Variable(tf.random_normal([2048,128])),
               'out':tf.Variable(tf.random_normal([128, n_classes]))}
    
    biases = {'b_conv1':tf.Variable(tf.random_normal([32])),
              'b_conv2':tf.Variable(tf.random_normal([64])),
              'b_fc':tf.Variable(tf.random_normal([128])),
              'out':tf.Variable(tf.random_normal([n_classes]))}
        
    x = tf.reshape(x, shape=[-1,14,32,1])
        
    conv1 = conv2d(x, weights['W_conv1']) + biases['b_conv1']
    conv1 = tf.nn.relu(conv1)
    conv1 = maxpool2d(conv1)
    print("conv1 shape:",conv1.shape)

    conv2 = conv2d(conv1, weights['W_conv2']) + biases['b_conv2']
    conv2 = tf.nn.relu(conv2)
    conv2 = maxpool2d(conv2)
    print("conv2 shape:",conv2.shape)
    
    fc = tf.reshape(conv2, [-1, 2048])
    fc = tf.matmul(fc, weights['W_fc']) + biases['b_fc']
    fc = tf.nn.relu(fc)
    fc = tf.nn.dropout(fc, dropout)
    print("fc shape:",fc.shape)
    
    output = tf.matmul(fc, weights['out']) + biases['out']
    print("output shape:",output.shape)
    return output

def train_model(x):
    prediction = cnn_model(x)

    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))

    optimizer = tf.train.AdamOptimizer().minimize(cost)
    
    [X_train, X_test, X_val, y_train, y_test, y_val] = get_data()
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        
        for epochs in range(hm_epochs):
            epoch_loss = 0
            for i in range(int(X_train.shape[0]/batch_size)):
                batch_x = X_train[i*batch_size:(i+1)*batch_size]
                batch_y = y_train[i*batch_size:(i+1)*batch_size]
                
                _, c = sess.run([optimizer, cost], feed_dict = {x: batch_x, y: batch_y})
                epoch_loss += c

            correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
            accuracy = tf.reduce_mean(tf.cast(correct,'float'))
            print('Epoch:', epochs+1, '/', hm_epochs, ' Loss:', epoch_loss, ' Accuracy:', accuracy.eval({x: X_val, y: y_val}))        
                
        correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
        accuracy = tf.reduce_mean(tf.cast(correct,'float'))
        print('Accuracy: ', accuracy.eval({x: X_test, y: y_test}))

if __name__ == '__main__':
    train_model(x)