In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing the code template and writeup template will cover all of the rubric points for this project.

The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.

Note:Code and Markdown cells can be executed using theShift + Enterkeyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.

In [1]:

```
# Load pickled data
import pickle
traffic_signs_data_folder = 'traffic-signs-data/'
training_file = traffic_signs_data_folder + 'train.p'
validation_file= traffic_signs_data_folder + 'valid.p'
testing_file = traffic_signs_data_folder + 'test.p'
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(validation_file, mode='rb') as f:
valid = pickle.load(f)
with open(testing_file, mode='rb') as f:
test = pickle.load(f)
X_train, y_train = train['features'], train['labels']
X_valid, y_valid = valid['features'], valid['labels']
X_test, y_test = test['features'], test['labels']
print ('Data loaded')
```

The pickled data is a dictionary with 4 key/value pairs:

`'features'`

is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).`'labels'`

is a 1D array containing the label/class id of the traffic sign. The file`signnames.csv`

contains id -> name mappings for each id.`'sizes'`

is a list containing tuples, (width, height) representing the original width and height the image.`'coords'`

is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image.**THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES**

Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the pandas shape method might be useful for calculating some of the summary results.

In [2]:

```
### Replace each question mark with the appropriate value.
### Use python, pandas or numpy methods rather than hard coding the results
import pandas as pd
import numpy as np
assert (len(X_train) == len(y_train))
assert (len(X_valid) == len(y_valid))
assert (len(X_test) == len(y_test))
# Number of training examples
n_train = len(X_train)
# Number of validation examples
n_validation = len(X_valid)
# Number of testing examples.
n_test = len(X_test)
# shape of an traffic sign image
image_shape = X_train[0].shape
# How many unique classes/labels there are in the dataset.
n_classes = np.unique(y_train).size
print("The size of training set is ", n_train)
print("The size of the validation set is ", n_validation)
print("The size of test set is ", n_test)
print("The shape of a traffic sign image is ", image_shape)
print("The number of unique classes/labels in the data set is =", n_classes)
```

Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.

The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.

**NOTE:** It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?

In [3]:

```
### Data exploration visualization code goes here.
### Feel free to use as many code cells as needed.
import matplotlib.pyplot as plt
# Visualizations will be shown in the notebook.
%matplotlib inline
```

In [4]:

```
for index in range(100,101):
image = X_train[index]
plt.figure(figsize=(1,1))
plt.imshow(image)
print(y_train[index])
```

In [5]:

```
# read the semantics of the numbers
import csv
import random
# Visualizations will be shown in the notebook.
%matplotlib inline
classId2SignName = {}
with open('signnames.csv') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
classId2SignName[row[0]] = row[1]
def visualizeImages(images,labels):
n_columns = 8
n_rows = int(len(images)/n_columns)+1
width = 24
height = n_rows * 3
fig, axs = plt.subplots(n_rows,n_columns, figsize=(width, height))
fig.subplots_adjust(hspace = .2, wspace=.1)
axs = axs.ravel()
for i in range(n_columns * n_rows):
axs[i].axis('off')
if (i<len(images)):
image = images[i]
axs[i].axis('off')
#if (image.shape[2] == 1):
# print(image)
# axs[i].imshow(image.astype(np.uint8).squeeze, cmap='gray')
#else:
axs[i].imshow(image)
axs[i].set_title('{} ({:.20})'.format(labels[i], classId2SignName[str(labels[i])]))
print ('done')
```

In [6]:

```
X = []
Y = []
labels, indices = np.unique(y_train, return_index=True)
for i in range(len(labels)):
X.append(X_train[indices[i]])
Y.append(labels[i])
visualizeImages(X,Y)
print ('done')
```

In [7]:

```
x,y = np.unique(y_train, return_counts=True)
plt.bar(x,y)
plt.show
print ('done')
```

Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.

The LeNet-5 implementation shown in the classroom at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!

With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission.

There are various aspects to consider when thinking about this problem:

- Neural network architecture (is the network over or underfitting?)
- Play around preprocessing techniques (normalization, rgb to grayscale, etc)
- Number of examples per label (some have more than others).
- Generate fake data.

Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.

Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, `(pixel - 128)/ 128`

is a quick way to approximately normalize the data and can be used in this project.

Other pre-processing steps are optional. You can try different techniques to see if it improves performance.

Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.

In [8]:

```
### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include
### converting to grayscale, etc.
### Feel free to use as many code cells as needed.
def preProcess(color_images):
# grayscale
grayscaled_images = np.sum(color_images/3, axis=3, keepdims=True)
# normalize
normalized_images = (grayscaled_images - 128) / 128
return normalized_images
print ('done')
```

In [9]:

```
def getRandomImage(x, y, filter_index):
indices, = np.nonzero( y==filter_index )
index = np.random.choice(indices)
return x[index]
image = getRandomImage(X_train, y_train, 23)
plt.figure(figsize=(1,1))
plt.imshow(image)
print ('done')
```

In [10]:

```
print (X_train.shape)
preprocessed = preProcess(X_train)
print (preprocessed.shape)
print ('done')
```

In [11]:

```
from sklearn.utils import shuffle
X_train, y_train = shuffle(X_train, y_train)
```

In [12]:

```
import tensorflow as tf
EPOCHS = 150
BATCH_SIZE = 128
DROPOUT = 0.5
```

In [13]:

```
keep_prob = tf.placeholder(tf.float32)
x = tf.placeholder(tf.float32, (None, 32, 32, 1))
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 43)
keep_prob = tf.placeholder(tf.float32)
```

In [14]:

```
from tensorflow.contrib.layers import flatten
def LeNet(x):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1
# Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))
conv1_b = tf.Variable(tf.zeros(6))
conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b
# Layer 1: Activation.
conv1 = tf.nn.relu(conv1)
# Layer 1: Pooling. Input = 28x28x6. Output = 14x14x6.
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# Layer 2: Convolutional. Output = 10x10x16.
conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
conv2_b = tf.Variable(tf.zeros(16))
conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b
# Layer 2: Activation.
conv2 = tf.nn.relu(conv2)
# Layer 2: Pooling. Input = 10x10x16. Output = 5x5x16.
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# Layer 2: Flatten. Input = 5x5x16. Output = 400.
fc0 = flatten(conv2)
# Layer 3: Fully Connected. Input = 400. Output = 120.
fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
fc1_b = tf.Variable(tf.zeros(120))
fc1 = tf.matmul(fc0, fc1_W) + fc1_b
# Layer 3: Activation.
fc1 = tf.nn.relu(fc1)
# Layer 4: Fully Connected. Input = 120. Output = 84.
fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
fc2_b = tf.Variable(tf.zeros(84))
fc2 = tf.matmul(fc1, fc2_W) + fc2_b
# Layer 4: Activation.
fc2 = tf.nn.relu(fc2)
# Layer 5: Fully Connected. Input = 84. Output = 43.
fc3_W = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))
fc3_b = tf.Variable(tf.zeros(43))
logits = tf.matmul(fc2, fc3_W) + fc3_b
return logits
```

In [15]:

```
from tensorflow.contrib.layers import flatten
def LeNet_modified(x):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1
# Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))
conv1_b = tf.Variable(tf.zeros(6))
conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b
# Layer 1: Activation.
conv1 = tf.nn.relu(conv1)
# Layer 1: Pooling. Input = 28x28x6. Output = 14x14x6.
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# Layer 2: Convolutional. Output = 10x10x16.
conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
conv2_b = tf.Variable(tf.zeros(16))
conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b
# Layer 2: Activation.
conv2 = tf.nn.relu(conv2)
# Layer 2: Pooling. Input = 10x10x16. Output = 5x5x16.
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# Layer 2: Flatten. Input = 5x5x16. Output = 400.
fc0 = flatten(conv2)
fc0 = tf.nn.dropout(fc0, keep_prob)
# Layer 3: Fully Connected. Input = 400. Output = 120.
fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
fc1_b = tf.Variable(tf.zeros(120))
fc1 = tf.matmul(fc0, fc1_W) + fc1_b
# Layer 3: Activation.
fc1 = tf.nn.relu(fc1)
fc1 = tf.nn.dropout(fc1, keep_prob)
# Layer 4: Fully Connected. Input = 120. Output = 84.
fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
fc2_b = tf.Variable(tf.zeros(84))
fc2 = tf.matmul(fc1, fc2_W) + fc2_b
# Layer 4: Activation.
fc2 = tf.nn.relu(fc2)
fc2 = tf.nn.dropout(fc2, keep_prob)
# Layer 5: Fully Connected. Input = 84. Output = 43.
fc3_W = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))
fc3_b = tf.Variable(tf.zeros(43))
logits = tf.matmul(fc2, fc3_W) + fc3_b
return logits
```

In [16]:

```
### Train your model here.
### Calculate and report the accuracy on the training and validation set.
### Once a final model architecture is selected,
### the accuracy on the test set should be calculated and reported as well.
### Feel free to use as many code cells as needed.
rate = 0.0006
logits = LeNet_modified(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
def evaluate(X_data, y_data):
num_examples = len(X_data)
total_accuracy = 0
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})
total_accuracy += (accuracy * len(batch_x))
return total_accuracy / num_examples
```

In [17]:

```
X_train_preProcessed = preProcess(X_train)
X_valid_preProcessed = preProcess(X_valid)
X_test_preProcessed = preProcess(X_test)
print ('done')
```

In [18]:

```
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_examples = len(X_train)
print("Training...")
print()
validation_accuracies = []
for i in range(EPOCHS):
X_train, X_train_preProcessed, y_train = shuffle(X_train, X_train_preProcessed, y_train)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH_SIZE
batch_x, batch_y = X_train_preProcessed[offset:end], y_train[offset:end]
sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: DROPOUT})
training_accuracy = evaluate(X_train_preProcessed, y_train)
validation_accuracy = evaluate(X_valid_preProcessed, y_valid)
print("EPOCH {} ...".format(i+1))
print("Training Accuracy = {:.3f}".format(training_accuracy))
print("Validation Accuracy = {:.3f}".format(validation_accuracy))
print()
validation_accuracies.append(validation_accuracy)
saver.save(sess, './lenet')
# show learning statistics
plt.plot(range(EPOCHS),validation_accuracies)
plt.show
print("Model saved")
```

In [19]:

```
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
test_accuracy = evaluate(X_test_preProcessed, y_test)
print("Test Accuracy = {:.3f}".format(test_accuracy))
```

To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.

You may find `signnames.csv`

useful as it contains mappings from the class id (integer) to the actual sign name.

In [20]:

```
### Load the images and plot them here.
### Feel free to use as many code cells as needed.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import glob
traffic_signs_data_folder = 'test_data/'
my_X_test = []
my_Y_test = []
for image_file in glob.glob(traffic_signs_data_folder + "*.jpg"):
image = np.array(mpimg.imread(image_file))
try:
#if (True):
# the label is encoded into the first two characters of the file names
label_str = image_file[len(traffic_signs_data_folder):][:2]
label = int(label_str)
my_X_test.append(image)
my_Y_test.append(label)
except Exception:
print ('Ignoring image {}'.format(image_file))
visualizeImages(my_X_test, my_Y_test)
print ('Done')
```

In [21]:

```
### Run the predictions here and use the model to output the prediction for each image.
### Make sure to pre-process the images with the same pre-processing pipeline used earlier.
### Feel free to use as many code cells as needed.
my_X_test_preProcessed = preProcess(np.array(my_X_test))
prediction=tf.argmax(logits,1)
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
predictions = sess.run(prediction, feed_dict={x: my_X_test_preProcessed, keep_prob: 1.0})
visualizeImages(my_X_test, predictions)
print (predictions)
```

In [22]:

```
### Calculate the accuracy for these 5 new images.
### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
test_accuracy = evaluate(my_X_test_preProcessed, my_Y_test)
print("Test Accuracy = {:.3f}".format(test_accuracy))
```

For each of the new images, print out the model's softmax probabilities to show the **certainty** of the model's predictions (limit the output to the top 5 probabilities for each image). `tf.nn.top_k`

could prove helpful here.

The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.

`tf.nn.top_k`

will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.

Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. `tf.nn.top_k`

is used to choose the three classes with the highest probability:

```
# (5, 6) array
a = np.array([[ 0.24879643, 0.07032244, 0.12641572, 0.34763842, 0.07893497,
0.12789202],
[ 0.28086119, 0.27569815, 0.08594638, 0.0178669 , 0.18063401,
0.15899337],
[ 0.26076848, 0.23664738, 0.08020603, 0.07001922, 0.1134371 ,
0.23892179],
[ 0.11943333, 0.29198961, 0.02605103, 0.26234032, 0.1351348 ,
0.16505091],
[ 0.09561176, 0.34396535, 0.0643941 , 0.16240774, 0.24206137,
0.09155967]])
```

Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))`

produces:

```
TopKV2(values=array([[ 0.34763842, 0.24879643, 0.12789202],
[ 0.28086119, 0.27569815, 0.18063401],
[ 0.26076848, 0.23892179, 0.23664738],
[ 0.29198961, 0.26234032, 0.16505091],
[ 0.34396535, 0.24206137, 0.16240774]]), indices=array([[3, 0, 5],
[0, 1, 4],
[0, 5, 1],
[1, 3, 5],
[1, 4, 3]], dtype=int32))
```

Looking just at the first row we get `[ 0.34763842, 0.24879643, 0.12789202]`

, you can confirm these are the 3 largest probabilities in `a`

. You'll also notice `[3, 0, 5]`

are the corresponding indices.

In [23]:

```
def visualizeTop_k(test_images, train_images, top_k):
probabilities = top_k.values
indices = top_k.indices
assert (len(test_images) == len(indices))
n_columns = 6
n_rows = len(indices)
width = 24
height = n_rows * 3
fig, axs = plt.subplots(n_rows,n_columns, figsize=(width, height))
fig.subplots_adjust(hspace = .2, wspace=.1)
axs = axs.ravel()
i = 0
for row in range(n_rows):
axs[i].axis('off')
axs[i].imshow(test_images[row])
#axs[i].set_title('{} ({:.20})'.format(labels[i], classId2SignName[str(labels[i])]))
i += 1
for j in range(len(indices[row])):
index = indices[row][j]
probability = probabilities[row][j]
axs[i].axis('off')
axs[i].set_title('{:.2f}% ({} {:.20})'.format(probability*100, index, classId2SignName[str(index)]))
axs[i].imshow(getRandomImage(X_train, y_train, index))
i += 1
print('done')
```

In [24]:

```
def visualizeTop_k2(test_images, train_images, top_k):
probabilities = top_k.values
indices = top_k.indices
assert (len(test_images) == len(indices))
n_columns = 2
n_rows = len(indices)
width = 24
height = n_rows * 3
fig, axs = plt.subplots(n_rows,n_columns, figsize=(width, height))
fig.subplots_adjust(hspace = .2, wspace=.1)
axs = axs.ravel()
i = 0
for row in range(n_rows):
axs[i].axis('off')
axs[i].imshow(test_images[row])
i += 1
labs=[classId2SignName[str(j)] for j in indices[row]]
axs[i].barh(labs, probabilities[row])
#axs[i].yticks(np.arange(1, 6, 1), labs)
i += 1
print('done')
```

In [25]:

```
### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web.
### Feel free to use as many code cells as needed.
softmax_logits = tf.nn.softmax(logits)
top_k=tf.nn.top_k(softmax_logits,5)
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
top_k = sess.run(top_k, feed_dict={x: my_X_test_preProcessed, keep_prob : 1.0})
visualizeTop_k(my_X_test, X_train, top_k)
visualizeTop_k2(my_X_test, X_train, top_k)
print('done')
```