In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.
Run the following cell to download the CIFAR-10 dataset for python.
In [1]:
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = block_num
if not isfile('cifar-10-python.tar.gz'):
with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
urlretrieve(
'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
'cifar-10-python.tar.gz',
pbar.hook)
if not isdir(cifar10_dataset_folder_path):
with tarfile.open('cifar-10-python.tar.gz') as tar:
tar.extractall()
tar.close()
tests.test_folder_path(cifar10_dataset_folder_path)
The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named data_batch_1, data_batch_2, etc.. Each batch contains the labels and images that are one of the following:
truck 10
Total 10 classes (Aras changed above/this section a bit)
Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the batch_id and sample_id. The batch_id is the id for a batch (1-5). The sample_id is the id for a image and label pair in the batch.
Ask yourself "What are all possible labels?", "What is the range of values for the image data?", "Are the labels in order or random?". Answers to questions like these will help you preprocess the data and end up with better predictions.
In [2]:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import helper
import numpy as np
# Explore the dataset
batch_id = 1
sample_id = 5
helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)
In [3]:
def normalize(x):
"""
Normalize a list of sample image data in the range of 0 to 1
: x: List of image data. The image shape is (32, 32, 3)
: return: Numpy array of normalize data
"""
# TODO: Implement Function
## image data shape = [t, i,j,k], t= num_img_per_batch (basically the list of images), i,j,k=height,width, and depth/channel
return x/255
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_normalize(normalize)
Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the one_hot_encode function. The input, x, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to one_hot_encode. Make sure to save the map of encodings outside the function.
Hint: Don't reinvent the wheel.
In [4]:
# import helper ## I did this because sklearn.preprocessing was defined in there
from sklearn import preprocessing ## from sklearn lib import preprocessing lib/sublib/functionality/class
def one_hot_encode(x):
"""
One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
: x: List of sample Labels
: return: Numpy array of one-hot encoded labels
"""
# TODO: Implement Function
## This was in the helper.py which belongs to the generic helper functions
# def display_image_predictions(features, labels, predictions):
# n_classes = 10
# label_names = _load_label_names()
# label_binarizer = LabelBinarizer()
# label_binarizer.fit(range(n_classes))
# label_ids = label_binarizer.inverse_transform(np.array(labels))
label_binarizer = preprocessing.LabelBinarizer() ## instantiate and initialized the one-hot encoder from class to one-hot
n_class = 10 ## total num_classes
label_binarizer.fit(range(n_class)) ## fit the one-vec to the range of number of classes, 10 in this case (dataset)
return label_binarizer.transform(x) ## transform the class labels to one-hot vec
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_one_hot_encode(one_hot_encode)
In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)
In [6]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle
import problem_unittests as tests
import helper
# Load the Preprocessed Validation data
valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))
For the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.
If you're finding it hard to dedicate enough time for this course a week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use TensorFlow Layers or TensorFlow Layers (contrib) to build each layer, except "Convolutional & Max Pooling" layer. TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.
If you would like to get the most of this course, try to solve all the problems without TF Layers. Let's begin!
The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions
neural_net_image_inputimage_shape with batch size set to None.name parameter in the TF Placeholder.neural_net_label_inputn_classes with batch size set to None.name parameter in the TF Placeholder.neural_net_keep_prob_inputname parameter in the TF Placeholder.These names will be used at the end of the project to load your saved model.
Note: None for shapes in TensorFlow allow for a dynamic size.
In [7]:
import tensorflow as tf
def neural_net_image_input(image_shape):
"""
Return a Tensor for a bach of image input
: image_shape: Shape of the images
: return: Tensor for image input.
"""
# TODO: Implement Function
## name the placeholder?? why should I do that? I can return it directly like below
## It is also worth mentioning that the overal image_shape = [i,j,k] meaning row, col, channels/depth or
## i, j, k
## h, w, depth (deep-wide learning)
## r, c, channels
## y, x, z
## Data_structure AKA data_shape are usually defined dshape = [i, j, k] as a tensor/Mat/Vec or even a scalar
## This is kind of tricky: image_shape is probablly pointing at the img_hight, img_width, and image_depth as well
## x_tensor is probably the input image or images or input batch
return tf.placeholder(tf.float32,
[None, image_shape[0], image_shape[1], image_shape[2]],
name='x')
def neural_net_label_input(n_classes):
"""
Return a Tensor for a batch of label input
: n_classes: Number of classes
: return: Tensor for label input.
"""
# TODO: Implement Function
## tf.placehoolder(data_type, data_structure (data_shape))
return tf.placeholder(tf.int32, [None, n_classes], name='y') ## the t/batch_size/num_img_per_batch = None & n/num_dimension = n_classes
def neural_net_keep_prob_input():
"""
Return a Tensor for keep probability
: return: Tensor for keep probability.
"""
# TODO: Implement Function
## Basically the main command should be like tf.placeholder(tf.float32, [None, 1]) since it is a scalar but I can also use a variable for it as well.
return tf.placeholder(dtype=tf.float32, name='keep_prob') ## this is basically a scalar but it is data_type/dtype is not INT but float since it is a probability value 0-1 (is it really??).
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tf.reset_default_graph()
tests.test_nn_image_inputs(neural_net_image_input)
tests.test_nn_label_inputs(neural_net_label_input)
tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)
Convolution layers have a lot of success with images. For this code cell, you should implement the function conv2d_maxpool to apply convolution then max pooling:
conv_ksize, conv_num_outputs and the shape of x_tensor.x_tensor using weight and conv_strides.pool_ksize and pool_strides.Note: You can't use TensorFlow Layers or TensorFlow Layers (contrib) for this layer. You're free to use any TensorFlow package for all the other layers.
In [8]:
def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):
"""
Apply convolution then max pooling to x_tensor
:param x_tensor: TensorFlow Tensor
:param conv_num_outputs: Number of outputs for the convolutional layer
:param conv_ksize: kernal size 2-D Tuple for the convolutional layer ## Aras: This might be probablly 3-D Tuple???
:param conv_strides: Stride 2-D Tuple for convolution
:param pool_ksize: kernal size 2-D Tuple for pool
:param pool_strides: Stride 2-D Tuple for pool
: return: A tensor that represents convolution and max pooling of x_tensor
"""
# TODO: Implement Function
x_tensor_shape = x_tensor.get_shape() ## x_tensor is 4D tensor [t/0, i/1, j/2, k/3] => x_tensor 4D tensor
k = x_tensor.get_shape()[3:4].num_elements() ## there shoudl be a simpler way!! num_elements/length
w_shape = [conv_ksize[0], conv_ksize[1], k, conv_num_outputs] # conv_num_output 1D tuple or a scalar
w = tf.Variable(tf.truncated_normal(w_shape, stddev=0.05))
## conv_kernel_strides
stride = [1, conv_strides[0], conv_strides[1], 1] # t == batch/num_data-img-images, i=hight/row/y, j=width/x/col, k=depth is usually 1
y_tensor = tf.nn.conv2d(x_tensor, w, stride, padding='VALID') ## apply convolution
## Let's create biases
# b = tf.Variable(tf.zeros(conv_num_outputs))
b = tf.zeros(conv_num_outputs)
#conv_layer = tf.nn.bias_add(conv_layer, bias) ## add biases
y_tensor += b
## adding relu function/activate function and the output is the h_tensor hidden layer output
h_tensor = tf.nn.relu(y_tensor) ## apply non-linearity, i.e. ReLU function
## pooling: can be max pooling or can be average pooling. Do not know any other kind.
## pool_ksize is a 2D tuple = [i, j]
kernel = [1, pool_ksize[0], pool_ksize[1], 1] # the same as stride, it is 4D tuple [batch=1, ksize, kchannels=1]
## pool_strides is a 2D tuple [i, j]
stride = [1, pool_strides[0], pool_ksize[1], 1] # [batch=1 (usually one input tensor per batch), ksize (2d tuple), k=1 (depth/num of channels)]
return tf.nn.max_pool(h_tensor, kernel, stride, padding='VALID')
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_con_pool(conv2d_maxpool)
Implement the flatten function to change the dimension of x_tensor from a 4-D tensor to a 2-D tensor. The output should be the shape (Batch Size, Flattened Image Size). You can use TensorFlow Layers or TensorFlow Layers (contrib) for this layer.
In [9]:
def flatten(x_tensor):
"""
Flatten x_tensor to (Batch Size, Flattened Image Size)
: x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
: return: A tensor of size (Batch Size, Flattened Image Size).
"""
# TODO: Implement Function
# x_tensor shape = [batch, img_w, img_height, img_depth]
# img_flat = img_w * img_h * img_d
n_size = x_tensor.get_shape()[1:4].num_elements() #1,2,3
## The final output should be x_tensor_flat = [t_size, n_size] in which t_size is totally unchanged
#t_size = x_tensor.get_shape()[0:1].num_elements() # only [0] which ic sthe first dimension/num_elements in 1st dimension
# now we should do reshape
return tf.reshape(x_tensor, [-1, n_size])
# def conv_net(x, weights, biases, dropout):
# # Layer 1 - 28*28*1 to 14*14*32
# conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# conv1 = maxpool2d(conv1, k=2)
# # Layer 2 - 14*14*32 to 7*7*64
# conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# conv2 = maxpool2d(conv2, k=2)
# # Fully connected layer - 7*7*64 to 1024
# fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
# fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
# fc1 = tf.nn.relu(fc1)
# fc1 = tf.nn.dropout(fc1, dropout)
# # Output Layer - class prediction - 1024 to 10
# out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
# return out
########## This is how Siraj implemented this layer.
# def flatten_layer(layer):
# # Get the shape of the input layer.
# layer_shape = layer.get_shape()
# # The shape of the input layer is assumed to be:
# # layer_shape == [num_images, img_height, img_width, num_channels]
# # The number of features is: img_height * img_width * num_channels
# # We can use a function from TensorFlow to calculate this.
# num_features = layer_shape[1:4].num_elements()
# # Reshape the layer to [num_images, num_features].
# # Note that we just set the size of the second dimension
# # to num_features and the size of the first dimension to -1
# # which means the size in that dimension is calculated
# # so the total size of the tensor is unchanged from the reshaping.
# layer_flat = tf.reshape(layer, [-1, num_features])
# # The shape of the flattened layer is now:
# # [num_images, img_height * img_width * num_channels]
# # Return both the flattened layer and the number of features.
# return layer_flat, num_features
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_flatten(flatten)
Implement the fully_conn function to apply a fully connected layer to x_tensor with the shape (Batch Size, num_outputs). You can use TensorFlow Layers or TensorFlow Layers (contrib) for this layer.
In [10]:
def fully_conn(x_tensor, num_outputs):
"""
Apply a fully connected layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
"""
# TODO: Implement Function
# This is basically a typical MLP input units and hidden units/ neurons with input data dimensions/variables
# w*x+b
## X_tensor is two dimensional tensor [t, n]. That is why w shape is [n, num_outputs]
## Since this is a FC (fully connected layer), n is 1 since the size of input units are equal to 1.
### In this layer, wx+b and then we apply ReLU/Sigmoid, and ..
## Let's define out w with variables
n_size = x_tensor.get_shape()[1:2].num_elements() # [t, n]
w = tf.Variable(tf.truncated_normal(shape=[n_size, num_outputs], stddev=0.05)) # normal dist function has mean and stddev
b = tf.zeros(num_outputs)
y_tensor = tf.matmul(x_tensor, w) + b
### Apply ReLU activatinon/non-linearity
return tf.nn.relu(y_tensor)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_fully_conn(fully_conn)
Implement the output function to apply a fully connected layer to x_tensor with the shape (Batch Size, num_outputs). You can use TensorFlow Layers or TensorFlow Layers (contrib) for this layer.
Note: Activation, softmax, or cross entropy shouldn't be applied to this.
In [11]:
def output(x_tensor, num_outputs):
"""
Apply a output layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
"""
# TODO: Implement Function
## The only differemce between this layer and the previous fully connected layer is the softmax for classification.
## Instead of ReLU, softmax should be used.
n_size = x_tensor.get_shape()[1:2].num_elements() ## to get the num_features
## Let's assign w and b for wx+b
w = tf.Variable(tf.truncated_normal(shape=[n_size, num_outputs], stddev=0.05))
b = tf.zeros(num_outputs)
return tf.matmul(x_tensor, w) + b
#return tf.nn.softmax(y_tensor) ## this should NOT be applied because in error with cross entropy softmax will be applied.
## That is why only logits wx+b is needed for this layer.
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_output(output)
Implement the function conv_net to create a convolutional neural network model. The function takes in a batch of images, x, and outputs logits. Use the layers you created above to create this model:
keep_prob.
In [18]:
def conv_net(x, keep_prob):
"""
Create a convolutional neural network model
: x: Placeholder tensor that holds image data.
: keep_prob: Placeholder tensor that hold dropout keep probability.
: return: Tensor that represents logits
"""
# TODO: Apply 1, 2, or 3 Convolution and Max Pool layers
# The 1st convolutional layer
h_tensor = conv2d_maxpool(x_tensor=x, conv_num_outputs=20, conv_ksize=[3, 3], conv_strides=[1, 1],
pool_ksize=[2, 2], pool_strides=[1, 1])
## The 2nd convolutional layer is added to increase the validation accuracy above 50%
h_tensor = conv2d_maxpool(x_tensor=h_tensor, conv_num_outputs=20, conv_ksize=[3, 3], conv_strides=[1, 1],
pool_ksize=[2, 2], pool_strides=[1, 1])
# TODO: Apply a Flatten Layer
x_tensor_flattened = flatten(x_tensor=h_tensor)
# TODO: Apply 1, 2, or 3 Fully Connected Layers
h_tensor = fully_conn(x_tensor=x_tensor_flattened, num_outputs=20)
## Where should Dropout be applied?
## tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)
h_tensor = tf.nn.dropout(x=h_tensor, keep_prob=keep_prob, noise_shape=None, seed=None, name=None)
# TODO: Apply an Output Layer
# TODO: return output
num_classes = 10 ## This is the toal number of classes for the clssification task
return output(x_tensor=h_tensor, num_outputs=num_classes)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
##############################
## Build the Neural Network ##
##############################
# Remove previous weights, bias, inputs, etc..
tf.reset_default_graph()
# Inputs
x = neural_net_image_input((32, 32, 3))
y = neural_net_label_input(10)
keep_prob = neural_net_keep_prob_input()
# Model
logits = conv_net(x, keep_prob)
# Name logits Tensor, so that is can be loaded from disk after training
logits = tf.identity(logits, name='logits')
# Loss and Optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
tests.test_conv_net(conv_net)
Implement the function train_neural_network to do a single optimization. The optimization should use optimizer to optimize in session with a feed_dict of the following:
x for image inputy for labelskeep_prob for keep probability for dropoutThis function will be called for each batch, so tf.global_variables_initializer() has already been called.
Note: Nothing needs to be returned. This function is only optimizing the neural network.
In [19]:
def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
"""
Optimize the session on a batch of images and labels
: session: Current TensorFlow session
: optimizer: TensorFlow optimizer function
: keep_probability: keep probability
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
"""
# TODO: Implement Function
# Feed the dictionary (x, y, dropout_prob) from Numpy (Python) into TensorFlow (Tensors)
feed_dict_train = {x:feature_batch, y:label_batch, keep_prob:keep_probability}
# Run the optimizer on the fed training dict (TF training data).
session.run(optimizer, feed_dict=feed_dict_train)
pass
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_train_nn(train_neural_network)
In [20]:
def print_stats(session, feature_batch, label_batch, cost, accuracy):
"""
Print information about loss and validation accuracy
: session: Current TensorFlow session
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
: cost: TensorFlow cost function
: accuracy: TensorFlow accuracy function
"""
# TODO: Implement Function
## Placeholders for input/feature and output/labels -> I am NOT sure if redefining the TFPlaceholders/tf.placeholders are neccessary again for this part.
## Feed_dict_train has to be redefined again I guess.
feed_dict_train = {x:feature_batch, y:label_batch, keep_prob:1.0}
## Print out loss using TF cost function in a session
cost_train = session.run(cost, feed_dict=feed_dict_train)
accuracy_train = session.run(accuracy, feed_dict=feed_dict_train)
print("trainging cost: ", cost_train, "accuracy: ", accuracy_train)
## Using the placeholders declared globally before and at the top
feed_dict_valid = {x:valid_features, y:valid_labels, keep_prob:1.0}
## Print out validation accuracy using TF accuracy function with valid_features and valid_labels
cost_valid = session.run(cost, feed_dict=feed_dict_valid)
accuracy_valid = session.run(accuracy, feed_dict=feed_dict_valid)
print("validation cost: ", cost_valid, "accuracy: ", accuracy_valid)
pass
Tune the following parameters:
epochs to the number of iterations until the network stops learning or start overfittingbatch_size to the highest number that your machine has memory for. Most people set them to common sizes of memory:keep_probability to the probability of keeping a node using dropout
In [21]:
# TODO: Tune Parameters
epochs = 100 #None
batch_size = 64 # recommanded as min #32 #L1d Cache from lsppu command #None
keep_probability = 0.5 #None
Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section.
In [22]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
print('Checking the Training on a Single Batch...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
batch_i = 1
for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')
print_stats(sess, batch_features, batch_labels, cost, accuracy)
In [23]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
save_model_path = './image_classification'
print('Training...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
# Loop over all batches
n_batches = 5
for batch_i in range(1, n_batches + 1):
for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')
print_stats(sess, batch_features, batch_labels, cost, accuracy)
# Save Model
saver = tf.train.Saver()
save_path = saver.save(sess, save_model_path)
In [20]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import tensorflow as tf
import pickle
import helper
import random
# Set batch size if not already set
try:
if batch_size:
pass
except NameError:
batch_size = 64
save_model_path = './image_classification'
n_samples = 4
top_n_predictions = 3
def test_model():
"""
Test the saved model against the test dataset
"""
test_features, test_labels = pickle.load(open('preprocess_training.p', mode='rb'))
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load model
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
# Get Tensors from loaded model
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for train_feature_batch, train_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
test_batch_acc_total += sess.run(
loaded_acc,
feed_dict={loaded_x: train_feature_batch, loaded_y: train_label_batch, loaded_keep_prob: 1.0})
test_batch_count += 1
print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))
# Print Random Samples
random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
random_test_predictions = sess.run(
tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)
test_model()
You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN. Pure guessing would get you 10% accuracy. However, you might notice people are getting scores well above 70%. That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_image_classification.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.