dog_app

Artificial Intelligence Nanodegree

Convolutional Neural Networks

Project: Write an Algorithm for a Dog Identification App


In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

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 will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.


Why We're Here

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

Sample Dog Output

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 6: Write your Algorithm
  • Step 7: Test Your Algorithm

Step 0: Import Datasets

Import Dog Dataset

In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:

  • train_files, valid_files, test_files - numpy arrays containing file paths to images
  • train_targets, valid_targets, test_targets - numpy arrays containing onehot-encoded classification labels
  • dog_names - list of string-valued dog breed names for translating labels
In [3]:
from sklearn.datasets import load_files       
from keras.utils import np_utils
import numpy as np
from glob import glob

# define function to load train, test, and validation datasets
def load_dataset(path):
    data = load_files(path)
    dog_files = np.array(data['filenames'])
    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
    return dog_files, dog_targets

# load train, test, and validation datasets
train_files, train_targets = load_dataset('/data/dog_images/train')
valid_files, valid_targets = load_dataset('/data/dog_images/valid')
test_files, test_targets = load_dataset('/data/dog_images/test')

# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("/data/dog_images/train/*/"))]

# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
Using TensorFlow backend.
There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.

Import Human Dataset

In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.

In [5]:
import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("/data/lfw/*/*"))
random.shuffle(human_files)

# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
There are 13233 total human images.

Step 1: Detect Humans

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.

In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [6]:
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

print(faces)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))
 
# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(105,0,255),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
[[ 66  67 119 119]]
Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [7]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

face_detector(human_files[3])
Out[7]:
True

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer:

In [8]:
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.
human = [h for h in human_files_short if face_detector(h)]
dogs = [d for d in dog_files_short if face_detector(d)]
human_percent = len(human)/len(human_files_short)*100.
dog_percent = len(dogs)/len(dog_files_short)*100.
print("Percentage of human is {}%".format(human_percent))
print("Percentage of dog is {}%".format(dog_percent))
Percentage of human is 100.0%
Percentage of dog is 11.0%

Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?

Answer:

We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.

In [10]:
! pip install face_recognition 
Collecting face_recognition
  Downloading https://files.pythonhosted.org/packages/28/10/f153bbbc218fc169768aa1c02f2e9178e9241e4af8da56289bdca2c0c217/face_recognition-1.2.2-py2.py3-none-any.whl
Requirement already satisfied: numpy in /opt/conda/lib/python3.6/site-packages (from face_recognition)
Collecting face-recognition-models>=0.3.0 (from face_recognition)
  Downloading https://files.pythonhosted.org/packages/cf/3b/4fd8c534f6c0d1b80ce0973d01331525538045084c73c153ee6df20224cf/face_recognition_models-0.3.0.tar.gz (100.1MB)
    100% |████████████████████████████████| 100.2MB 6.5kB/s eta 0:00:01   13% |████▍                           | 13.6MB 41.5MB/s eta 0:00:03    40% |█████████████                   | 40.5MB 46.7MB/s eta 0:00:02    56% |██████████████████              | 56.6MB 29.4MB/s eta 0:00:02    91% |█████████████████████████████▏  | 91.4MB 47.8MB/s eta 0:00:01
Requirement already satisfied: Pillow in /opt/conda/lib/python3.6/site-packages (from face_recognition)
Collecting dlib>=19.7 (from face_recognition)
  Downloading https://files.pythonhosted.org/packages/df/aa/6a9bb2a763107bb2606d6ee1aa65fcd3b51375a9ef6436e9c9280b0dd63c/dlib-19.15.0.tar.gz (3.3MB)
    100% |████████████████████████████████| 3.3MB 206kB/s eta 0:00:01
Requirement already satisfied: Click>=6.0 in /opt/conda/lib/python3.6/site-packages (from face_recognition)
Requirement already satisfied: olefile in /opt/conda/lib/python3.6/site-packages (from Pillow->face_recognition)
Building wheels for collected packages: face-recognition-models, dlib
  Running setup.py bdist_wheel for face-recognition-models ... done
  Stored in directory: /root/.cache/pip/wheels/d2/99/18/59c6c8f01e39810415c0e63f5bede7d83dfb0ffc039865465f
  Running setup.py bdist_wheel for dlib ... done
  Stored in directory: /root/.cache/pip/wheels/8e/8d/4f/6a4ee32422131b30f5e483f821e359a32b5cd7abbb22dda22d
Successfully built face-recognition-models dlib
Installing collected packages: face-recognition-models, dlib, face-recognition
Successfully installed dlib-19.15.0 face-recognition-1.2.2 face-recognition-models-0.3.0
You are using pip version 9.0.1, however version 18.0 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
In [11]:
import face_recognition
In [12]:
## (Optional) TODO: Report the performance of another  
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.
def face_detector_2(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_recognition.face_locations(gray)
    return len(faces) > 0
In [13]:
human_2 = [h for h in human_files_short if face_detector_2(h)]
dogs_2 = [d for d in dog_files_short if face_detector_2(d)]
human_percent_2 = len(human_2)/len(human_files_short)*100.
dog_percent_2 = len(dogs_2)/len(dog_files_short)*100.
print("Percentage of human is {}%".format(human_percent_2))
print("Percentage of dog is {}%".format(dog_percent_2))
Percentage of human is 100.0%
Percentage of dog is 9.0%
In [ ]:
Performance using this metric is shown below:
    
1. Percentage of human is 100.0%
2. Percentage of dog is 9.0%

Step 2: Detect Dogs

In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.

In [15]:
from keras.applications.resnet50 import ResNet50

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5
102858752/102853048 [==============================] - 2s 0us/step

Pre-process the Data

When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape

$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$

where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.

The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape

$$ (1, 224, 224, 3). $$

The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape

$$ (\text{nb_samples}, 224, 224, 3). $$

Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!

In [16]:
from keras.preprocessing import image                  
from tqdm import tqdm

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)

Making Predictions with ResNet-50

Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.

Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.

By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.

In [17]:
from keras.applications.resnet50 import preprocess_input, decode_predictions

def ResNet50_predict_labels(img_path):
    # returns prediction vector for image located at img_path
    img = preprocess_input(path_to_tensor(img_path))
    return np.argmax(ResNet50_model.predict(img))

Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).

We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [18]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151)) 

(IMPLEMENTATION) Assess the Dog Detector

Question 3: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer:

In [19]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.

human = [h for h in human_files_short if dog_detector(h)]
dogs = [d for d in dog_files_short if dog_detector(d)]
human_percent = len(human)/len(human_files_short)*100.
dog_percent = len(dogs)/len(dog_files_short)*100.
print("Percentage of human is {}%".format(human_percent))
print("Percentage of dog is {}%".format(dog_percent))
Percentage of human is 0.0%
Percentage of dog is 100.0%

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

Pre-process the Data

We rescale the images by dividing every pixel in every image by 255.

In [20]:
from PIL import ImageFile                            
ImageFile.LOAD_TRUNCATED_IMAGES = True                 

# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
100%|██████████| 6680/6680 [01:12<00:00, 91.79it/s] 
100%|██████████| 835/835 [00:08<00:00, 103.69it/s]
100%|██████████| 836/836 [00:08<00:00, 102.28it/s]

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    model.summary()

We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:

Sample CNN

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.

Answer:

In [21]:
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential

model = Sequential()

### TODO: Define your architecture.

model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(224,224,3)))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Flatten())
model.add(Dense(500, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(len(dog_names), activation='softmax'))

model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 224, 224, 16)      208       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 112, 112, 16)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 112, 112, 32)      2080      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 56, 56, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 64)        8256      
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 28, 28, 64)        0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 50176)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 500)               25088500  
_________________________________________________________________
dropout_1 (Dropout)          (None, 500)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 133)               66633     
=================================================================
Total params: 25,165,677
Trainable params: 25,165,677
Non-trainable params: 0
_________________________________________________________________

Compile the Model

In [22]:
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [23]:
from keras.callbacks import ModelCheckpoint  

### TODO: specify the number of epochs that you would like to use to train the model.

epochs = 5

### Do NOT modify the code below this line.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', 
                               verbose=1, save_best_only=True)

model.fit(train_tensors, train_targets, 
          validation_data=(valid_tensors, valid_targets),
          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples
Epoch 1/5
6660/6680 [============================>.] - ETA: 0s - loss: 4.8529 - acc: 0.0180Epoch 00001: val_loss improved from inf to 4.55749, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 30s 4ms/step - loss: 4.8516 - acc: 0.0183 - val_loss: 4.5575 - val_acc: 0.0443
Epoch 2/5
6660/6680 [============================>.] - ETA: 0s - loss: 4.3407 - acc: 0.0587Epoch 00002: val_loss improved from 4.55749 to 4.23726, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 29s 4ms/step - loss: 4.3395 - acc: 0.0585 - val_loss: 4.2373 - val_acc: 0.0695
Epoch 3/5
6660/6680 [============================>.] - ETA: 0s - loss: 3.8330 - acc: 0.1264Epoch 00003: val_loss improved from 4.23726 to 4.12015, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 29s 4ms/step - loss: 3.8338 - acc: 0.1263 - val_loss: 4.1202 - val_acc: 0.0838
Epoch 4/5
6660/6680 [============================>.] - ETA: 0s - loss: 3.0071 - acc: 0.2857Epoch 00004: val_loss did not improve
6680/6680 [==============================] - 29s 4ms/step - loss: 3.0067 - acc: 0.2858 - val_loss: 4.3164 - val_acc: 0.0826
Epoch 5/5
6660/6680 [============================>.] - ETA: 0s - loss: 1.9566 - acc: 0.5077Epoch 00005: val_loss did not improve
6680/6680 [==============================] - 29s 4ms/step - loss: 1.9568 - acc: 0.5075 - val_loss: 4.6923 - val_acc: 0.0850
Out[23]:
<keras.callbacks.History at 0x7f1f39f595c0>

Load the Model with the Best Validation Loss

In [24]:
model.load_weights('saved_models/weights.best.from_scratch.hdf5')

Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.

In [25]:
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]

# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 10.4067%

Step 4: Use a CNN to Classify Dog Breeds

To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning. In the following step, you will get a chance to use transfer learning to train your own CNN.

Obtain Bottleneck Features

In [26]:
bottleneck_features = np.load('/data/bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']

Model Architecture

The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.

In [27]:
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))

VGG16_model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_1 ( (None, 512)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 133)               68229     
=================================================================
Total params: 68,229
Trainable params: 68,229
Non-trainable params: 0
_________________________________________________________________

Compile the Model

In [28]:
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

Train the Model

In [29]:
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', 
                               verbose=1, save_best_only=True)

VGG16_model.fit(train_VGG16, train_targets, 
          validation_data=(valid_VGG16, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples
Epoch 1/20
6600/6680 [============================>.] - ETA: 0s - loss: 12.0423 - acc: 0.1289Epoch 00001: val_loss improved from inf to 10.27795, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 306us/step - loss: 12.0203 - acc: 0.1296 - val_loss: 10.2779 - val_acc: 0.2335
Epoch 2/20
6560/6680 [============================>.] - ETA: 0s - loss: 9.5980 - acc: 0.2956Epoch 00002: val_loss improved from 10.27795 to 9.35964, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 253us/step - loss: 9.5981 - acc: 0.2967 - val_loss: 9.3596 - val_acc: 0.3126
Epoch 3/20
6520/6680 [============================>.] - ETA: 0s - loss: 8.9265 - acc: 0.3739Epoch 00003: val_loss improved from 9.35964 to 9.08586, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 247us/step - loss: 8.9255 - acc: 0.3740 - val_loss: 9.0859 - val_acc: 0.3485
Epoch 4/20
6660/6680 [============================>.] - ETA: 0s - loss: 8.5955 - acc: 0.4078Epoch 00004: val_loss improved from 9.08586 to 8.70915, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 244us/step - loss: 8.5892 - acc: 0.4081 - val_loss: 8.7092 - val_acc: 0.3760
Epoch 5/20
6500/6680 [============================>.] - ETA: 0s - loss: 8.2002 - acc: 0.4455Epoch 00005: val_loss improved from 8.70915 to 8.51110, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 247us/step - loss: 8.2151 - acc: 0.4446 - val_loss: 8.5111 - val_acc: 0.3928
Epoch 6/20
6660/6680 [============================>.] - ETA: 0s - loss: 7.9775 - acc: 0.4682Epoch 00006: val_loss improved from 8.51110 to 8.45040, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 245us/step - loss: 7.9856 - acc: 0.4677 - val_loss: 8.4504 - val_acc: 0.3964
Epoch 7/20
6540/6680 [============================>.] - ETA: 0s - loss: 7.8621 - acc: 0.4830Epoch 00007: val_loss improved from 8.45040 to 8.31532, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 247us/step - loss: 7.8638 - acc: 0.4831 - val_loss: 8.3153 - val_acc: 0.4120
Epoch 8/20
6600/6680 [============================>.] - ETA: 0s - loss: 7.7794 - acc: 0.4932Epoch 00008: val_loss improved from 8.31532 to 8.29558, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 245us/step - loss: 7.7789 - acc: 0.4933 - val_loss: 8.2956 - val_acc: 0.4108
Epoch 9/20
6520/6680 [============================>.] - ETA: 0s - loss: 7.6068 - acc: 0.5090Epoch 00009: val_loss improved from 8.29558 to 8.08962, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 247us/step - loss: 7.6222 - acc: 0.5084 - val_loss: 8.0896 - val_acc: 0.4299
Epoch 10/20
6520/6680 [============================>.] - ETA: 0s - loss: 7.4760 - acc: 0.5094Epoch 00010: val_loss improved from 8.08962 to 7.95231, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 246us/step - loss: 7.4537 - acc: 0.5100 - val_loss: 7.9523 - val_acc: 0.4347
Epoch 11/20
6560/6680 [============================>.] - ETA: 0s - loss: 7.2578 - acc: 0.5264Epoch 00011: val_loss improved from 7.95231 to 7.88616, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 247us/step - loss: 7.2614 - acc: 0.5262 - val_loss: 7.8862 - val_acc: 0.4132
Epoch 12/20
6520/6680 [============================>.] - ETA: 0s - loss: 7.1787 - acc: 0.5365Epoch 00012: val_loss improved from 7.88616 to 7.85335, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 247us/step - loss: 7.1913 - acc: 0.5358 - val_loss: 7.8533 - val_acc: 0.4455
Epoch 13/20
6580/6680 [============================>.] - ETA: 0s - loss: 7.1052 - acc: 0.5450Epoch 00013: val_loss improved from 7.85335 to 7.82256, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 245us/step - loss: 7.1132 - acc: 0.5443 - val_loss: 7.8226 - val_acc: 0.4419
Epoch 14/20
6560/6680 [============================>.] - ETA: 0s - loss: 7.0198 - acc: 0.5465Epoch 00014: val_loss improved from 7.82256 to 7.70212, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 245us/step - loss: 7.0199 - acc: 0.5464 - val_loss: 7.7021 - val_acc: 0.4443
Epoch 15/20
6440/6680 [===========================>..] - ETA: 0s - loss: 6.8997 - acc: 0.5576Epoch 00015: val_loss improved from 7.70212 to 7.69536, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 244us/step - loss: 6.9190 - acc: 0.5563 - val_loss: 7.6954 - val_acc: 0.4467
Epoch 16/20
6560/6680 [============================>.] - ETA: 0s - loss: 6.8174 - acc: 0.5616Epoch 00016: val_loss improved from 7.69536 to 7.64954, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 246us/step - loss: 6.8320 - acc: 0.5608 - val_loss: 7.6495 - val_acc: 0.4539
Epoch 17/20
6620/6680 [============================>.] - ETA: 0s - loss: 6.6205 - acc: 0.5707Epoch 00017: val_loss improved from 7.64954 to 7.51852, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 246us/step - loss: 6.6251 - acc: 0.5705 - val_loss: 7.5185 - val_acc: 0.4503
Epoch 18/20
6560/6680 [============================>.] - ETA: 0s - loss: 6.4986 - acc: 0.5822Epoch 00018: val_loss improved from 7.51852 to 7.41968, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 246us/step - loss: 6.5183 - acc: 0.5807 - val_loss: 7.4197 - val_acc: 0.4551
Epoch 19/20
6540/6680 [============================>.] - ETA: 0s - loss: 6.4423 - acc: 0.5902Epoch 00019: val_loss improved from 7.41968 to 7.40869, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 246us/step - loss: 6.4571 - acc: 0.5891 - val_loss: 7.4087 - val_acc: 0.4599
Epoch 20/20
6560/6680 [============================>.] - ETA: 0s - loss: 6.3861 - acc: 0.5880Epoch 00020: val_loss improved from 7.40869 to 7.31908, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 246us/step - loss: 6.3753 - acc: 0.5885 - val_loss: 7.3191 - val_acc: 0.4539
Out[29]:
<keras.callbacks.History at 0x7f1f320224a8>

Load the Model with the Best Validation Loss

In [30]:
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')

Test the Model

Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.

In [31]:
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]

# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 47.0096%

Predict Dog Breed with the Model

In [32]:
from extract_bottleneck_features import *

def VGG16_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = VGG16_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras. These are already in the workspace, at /data/bottleneck_features. If you wish to download them on a different machine, they can be found at:

The files are encoded as such:

Dog{network}Data.npz

where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception.

The above architectures are downloaded and stored for you in the /data/bottleneck_features/ folder.

This means the following will be in the /data/bottleneck_features/ folder:

DogVGG19Data.npz DogResnet50Data.npz DogInceptionV3Data.npz DogXceptionData.npz

(IMPLEMENTATION) Obtain Bottleneck Features

In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:

bottleneck_features = np.load('/data/bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
In [33]:
### TODO: Obtain bottleneck features from another pre-trained CNN.

bottleneck_features = np.load('/data/bottleneck_features/DogInceptionV3Data.npz')
train_V3Data = bottleneck_features['train']
valid_V3Data = bottleneck_features['valid']
test_V3Data = bottleneck_features['test']

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    <your model's name>.summary()

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer:

In [34]:
### TODO: Define your architecture.

V3Data_model = Sequential()
V3Data_model.add(GlobalAveragePooling2D(input_shape=train_V3Data.shape[1:]))
V3Data_model.add(Dropout(0.4))
V3Data_model.add(Dense(133, activation='softmax'))

V3Data_model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_2 ( (None, 2048)              0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 2048)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 133)               272517    
=================================================================
Total params: 272,517
Trainable params: 272,517
Non-trainable params: 0
_________________________________________________________________

(IMPLEMENTATION) Compile the Model

In [35]:
### TODO: Compile the model.
V3Data_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [36]:
### TODO: Train the model.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.V3Data.hdf5', 
                               verbose=1, save_best_only=True)

V3Data_model.fit(train_V3Data, train_targets, 
          validation_data=(valid_V3Data, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples
Epoch 1/20
6540/6680 [============================>.] - ETA: 0s - loss: 1.4056 - acc: 0.6613Epoch 00001: val_loss improved from inf to 0.64912, saving model to saved_models/weights.best.V3Data.hdf5
6680/6680 [==============================] - 3s 380us/step - loss: 1.3936 - acc: 0.6635 - val_loss: 0.6491 - val_acc: 0.8072
Epoch 2/20
6620/6680 [============================>.] - ETA: 0s - loss: 0.5828 - acc: 0.8275Epoch 00002: val_loss improved from 0.64912 to 0.60532, saving model to saved_models/weights.best.V3Data.hdf5
6680/6680 [==============================] - 2s 337us/step - loss: 0.5862 - acc: 0.8268 - val_loss: 0.6053 - val_acc: 0.8455
Epoch 3/20
6620/6680 [============================>.] - ETA: 0s - loss: 0.5071 - acc: 0.8577Epoch 00003: val_loss did not improve
6680/6680 [==============================] - 2s 336us/step - loss: 0.5065 - acc: 0.8578 - val_loss: 0.6490 - val_acc: 0.8359
Epoch 4/20
6540/6680 [============================>.] - ETA: 0s - loss: 0.4638 - acc: 0.8694Epoch 00004: val_loss did not improve
6680/6680 [==============================] - 2s 338us/step - loss: 0.4642 - acc: 0.8695 - val_loss: 0.6315 - val_acc: 0.8443
Epoch 5/20
6560/6680 [============================>.] - ETA: 0s - loss: 0.4171 - acc: 0.8887Epoch 00005: val_loss did not improve
6680/6680 [==============================] - 2s 334us/step - loss: 0.4189 - acc: 0.8877 - val_loss: 0.6427 - val_acc: 0.8587
Epoch 6/20
6600/6680 [============================>.] - ETA: 0s - loss: 0.3824 - acc: 0.8953Epoch 00006: val_loss did not improve
6680/6680 [==============================] - 2s 336us/step - loss: 0.3821 - acc: 0.8958 - val_loss: 0.7243 - val_acc: 0.8491
Epoch 7/20
6560/6680 [============================>.] - ETA: 0s - loss: 0.3609 - acc: 0.8983Epoch 00007: val_loss did not improve
6680/6680 [==============================] - 2s 333us/step - loss: 0.3586 - acc: 0.8987 - val_loss: 0.6880 - val_acc: 0.8563
Epoch 8/20
6600/6680 [============================>.] - ETA: 0s - loss: 0.3486 - acc: 0.9082Epoch 00008: val_loss did not improve
6680/6680 [==============================] - 2s 335us/step - loss: 0.3502 - acc: 0.9079 - val_loss: 0.6833 - val_acc: 0.8611
Epoch 9/20
6660/6680 [============================>.] - ETA: 0s - loss: 0.3361 - acc: 0.9147Epoch 00009: val_loss did not improve
6680/6680 [==============================] - 2s 334us/step - loss: 0.3358 - acc: 0.9148 - val_loss: 0.7501 - val_acc: 0.8467
Epoch 10/20
6540/6680 [============================>.] - ETA: 0s - loss: 0.3148 - acc: 0.9190Epoch 00010: val_loss did not improve
6680/6680 [==============================] - 2s 336us/step - loss: 0.3153 - acc: 0.9193 - val_loss: 0.6820 - val_acc: 0.8539
Epoch 11/20
6660/6680 [============================>.] - ETA: 0s - loss: 0.3004 - acc: 0.9201Epoch 00011: val_loss did not improve
6680/6680 [==============================] - 2s 333us/step - loss: 0.3010 - acc: 0.9199 - val_loss: 0.7639 - val_acc: 0.8623
Epoch 12/20
6560/6680 [============================>.] - ETA: 0s - loss: 0.2845 - acc: 0.9242Epoch 00012: val_loss did not improve
6680/6680 [==============================] - 2s 337us/step - loss: 0.2844 - acc: 0.9243 - val_loss: 0.7742 - val_acc: 0.8587
Epoch 13/20
6520/6680 [============================>.] - ETA: 0s - loss: 0.2608 - acc: 0.9311Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 2s 334us/step - loss: 0.2646 - acc: 0.9310 - val_loss: 0.8045 - val_acc: 0.8623
Epoch 14/20
6560/6680 [============================>.] - ETA: 0s - loss: 0.2643 - acc: 0.9326Epoch 00014: val_loss did not improve
6680/6680 [==============================] - 2s 332us/step - loss: 0.2647 - acc: 0.9323 - val_loss: 0.7881 - val_acc: 0.8599
Epoch 15/20
6580/6680 [============================>.] - ETA: 0s - loss: 0.2530 - acc: 0.9356Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 2s 332us/step - loss: 0.2539 - acc: 0.9352 - val_loss: 0.8274 - val_acc: 0.8551
Epoch 16/20
6520/6680 [============================>.] - ETA: 0s - loss: 0.2496 - acc: 0.9370Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 2s 334us/step - loss: 0.2460 - acc: 0.9377 - val_loss: 0.8571 - val_acc: 0.8467
Epoch 17/20
6660/6680 [============================>.] - ETA: 0s - loss: 0.2600 - acc: 0.9353Epoch 00017: val_loss did not improve
6680/6680 [==============================] - 2s 336us/step - loss: 0.2592 - acc: 0.9355 - val_loss: 0.8630 - val_acc: 0.8587
Epoch 18/20
6580/6680 [============================>.] - ETA: 0s - loss: 0.2371 - acc: 0.9410Epoch 00018: val_loss did not improve
6680/6680 [==============================] - 2s 333us/step - loss: 0.2375 - acc: 0.9413 - val_loss: 0.8348 - val_acc: 0.8563
Epoch 19/20
6660/6680 [============================>.] - ETA: 0s - loss: 0.2339 - acc: 0.9398Epoch 00019: val_loss did not improve
6680/6680 [==============================] - 2s 336us/step - loss: 0.2333 - acc: 0.9400 - val_loss: 0.8808 - val_acc: 0.8515
Epoch 20/20
6620/6680 [============================>.] - ETA: 0s - loss: 0.2309 - acc: 0.9412Epoch 00020: val_loss did not improve
6680/6680 [==============================] - 2s 336us/step - loss: 0.2325 - acc: 0.9409 - val_loss: 0.8833 - val_acc: 0.8587
Out[36]:
<keras.callbacks.History at 0x7f1f31e36b38>

(IMPLEMENTATION) Load the Model with the Best Validation Loss

In [37]:
### TODO: Load the model weights with the best validation loss.
V3Data_model.load_weights('saved_models/weights.best.V3Data.hdf5')

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.

In [38]:
### TODO: Calculate classification accuracy on the test dataset.
V3Data_predictions = [np.argmax(V3Data_model.predict(np.expand_dims(feature, axis=0))) for feature in test_V3Data]

# report test accuracy
test_accuracy = 100*np.sum(np.array(V3Data_predictions)==np.argmax(test_targets, axis=1))/len(V3Data_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 78.8278%

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.

Similar to the analogous function in Step 5, your function should have three steps:

  1. Extract the bottleneck features corresponding to the chosen CNN model.
  2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
  3. Use the dog_names array defined in Step 0 of this notebook to return the corresponding breed.

The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function

extract_{network}

where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.

In [39]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.

from extract_bottleneck_features import *

def V3Data_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_InceptionV3(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = V3Data_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

Step 6: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.

Some sample output for our algorithm is provided below, but feel free to design your own user experience!

Sample Human Output

(IMPLEMENTATION) Write your Algorithm

In [55]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.

def make_predictions(img):
    dog = dog_detector(img)
    human = face_detector(img)
    dog_breed = V3Data_predict_breed(img)
    if dog:
        img1 = cv2.imread(img)
        cv_rgb = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
        plt.imshow(cv_rgb)
        print("The predicted breed of the dog is {}".format(dog_breed[7:]))
    elif human:
        img1 = cv2.imread(img)
        cv_rgb = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
        plt.imshow(cv_rgb)
        print("Human is detected in the image")
        print("This human looks like a {} dog".format(dog_breed[7:]))
    else:
        img1 = cv2.imread(img)
        cv_rgb = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
        plt.imshow(cv_rgb)
        print("There is neither a human or a dog in the image provided")



make_predictions(train_files[2])
The predicted breed of the dog is Irish_water_spaniel
In [47]:
make_predictions(human_files[3])
Human is detected in the image
This human looks like a Chinese_crested dog

Step 7: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected 🙂 ? Or worse 🙁 ? Provide at least three possible points of improvement for your algorithm.

Answer:

In [48]:
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
make_predictions("images/me1.PNG")
Human is detected in the image
This human looks like a Dachshund dog
In [49]:
make_predictions("images/me2.PNG")
Human is detected in the image
This human looks like a Chinese_crested dog
In [50]:
make_predictions("images/me_dog1.PNG")
The predicted breed of the dog is German_shepherd_dog
In [51]:
make_predictions("images/me_dog2.PNG")
The predicted breed of the dog is Chihuahua