gonzaloruizdevilla

embeddings = tf.Variable(
    tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))

gonzaloruizdevilla

output_weights = tf.Variable(
  tf.truncated_normal([vocabulary_size, embedding_size],
                      stddev=1.0 / math.sqrt(embedding_size)))
output_biases = tf.Variable(tf.zeros([vocabulary_size]))

gonzaloruizdevilla

# Placeholders for inputs
train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])

gonzaloruizdevilla

embed = tf.nn.embedding_lookup(embeddings, train_inputs)

gonzaloruizdevilla

# Compute the NCE loss, using a sample of the negative labels each time.
loss = tf.reduce_mean(
  tf.nn.nce_loss(weights=nce_weights,
                 biases=nce_biases,
                 labels=train_labels,
                 inputs=embed,
                 num_sampled=num_sampled,
                 num_classes=vocabulary_size))

gonzaloruizdevilla

# We use the SGD optimizer.
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0).minimize(loss)

gonzaloruizdevilla

for inputs, labels in generate_batch(...):
  feed_dict = {train_inputs: inputs, train_labels: labels}
  _, cur_loss = session.run([optimizer, loss], feed_dict=feed_dict)

gonzaloruizdevilla

import pickle
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Read in the saved camera matrix and distortion coefficients
# These are the arrays you calculated using cv2.calibrateCamera()
dist_pickle = pickle.load( open( "wide_dist_pickle.p", "rb" ) )
mtx = dist_pickle["mtx"]

gonzaloruizdevilla

import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle


# Read in an image and grayscale it
image = mpimg.imread('signs_vehicles_xygrad.png')

gonzaloruizdevilla

import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle


# Read in an image
image = mpimg.imread('signs_vehicles_xygrad.png')