import tensorflow as tf
from Trainer import Trainer, parse_args
import os
data_path, epochs = parse_args()
sess = tf.InteractiveSession(config=tf.ConfigProto())
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
x = tf.placeholder(tf.float32, shape=[None, 240, 320, 3])
y_ = tf.placeholder(tf.float32, shape=[None, 3])
x_shaped = tf.reshape(x, [-1, 240 * 320 * 3])
W = tf.Variable(tf.zeros([240 * 320 * 3, 3]))
b = tf.Variable(tf.zeros([3]))
y = tf.nn.softmax(tf.matmul(x_shaped, W) + b)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
model_file = os.path.dirname(os.path.realpath(__file__)) + '/' + os.path.basename(__file__)
import tensorflow as tf
from Trainer import Trainer, parse_args
import os
from model import *
args = parse_args()
data_path = args["datapath"]
epochs = args["epochs"]
s3_bucket = args['s3_bucket']
show_speed = args['show_speed']
s3_sync = args['s3_sync']
sess = tf.InteractiveSession(config=tf.ConfigProto())
x = tf.placeholder(tf.float32, shape=[None, 240, 320, 3], name='x')
y_ = tf.placeholder(tf.float32, shape=[None, 3], name='y_')
W_conv1 = weight_variable('layer1', [6, 6, 3, 24])
b_conv1 = bias_variable('layer1', [24])
h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable('layer2', [6, 6, 24, 24])
b_conv2 = bias_variable('layer2', [24])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
W_conv3 = weight_variable('layer3', [6, 6, 24, 36])
b_conv3 = bias_variable('layer3', [36])
h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv3) + b_conv3)
h_pool3 = max_pool_2x2(h_conv3)
import tensorflow as tf
from Trainer import Trainer, parse_args
import os
from model import *
args = parse_args()
data_path = args["datapath"]
epochs = args["epochs"]
s3_bucket = args['s3_bucket']
show_speed = args['show_speed']
s3_sync = args['s3_sync']
sess = tf.InteractiveSession(config=tf.ConfigProto())
x = tf.placeholder(tf.float32, shape=[None, 240, 320, 3], name='x')
y_ = tf.placeholder(tf.float32, shape=[None, 3], name='y_')
W_conv1 = weight_variable('layer1',[6, 6, 3, 32])
b_conv1 = bias_variable('layer1',[32])
h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_fc1 = weight_variable('layer2',[120 * 160 * 32, 512])
b_fc1 = bias_variable('layer2',[512])
h_pool1_flat = tf.reshape(h_pool1, [-1, 120 * 160 * 32])
h_fc1 = tf.nn.relu(tf.matmul(h_pool1_flat, W_fc1) + b_fc1)
dropout_keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, dropout_keep_prob)
