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!).

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 [1]:
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('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')

# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/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 [2]:
import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("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 [3]:
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 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),(255,0,0),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()


Number of faces detected: 3

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 [4]:
# 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

(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: 99% of the images from the human file where classified as human a human face and 11% of human face images where found in the dog_files


In [17]:
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.

humans = [path for path in human_files_short if face_detector(path)]
dogs = [path for path in dog_files_short if face_detector(path)]

print('There are %d out of %d where detected human faces the %.1f%%' %(len(humans),len(human_files_short),(100* len(humans)/len(human_files_short))))
print('There are %d out of %d where detected dogs the %.1f%%' %(len(dogs),len(dog_files_short),(100* len(dogs)/len(dog_files_short))))

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.


There are 99 out of 100 where detected human faces the 99.0%
There are 11 out of 100 where detected dogs the 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: Yes I belive is a reasonable to ask this of the user, it gives a contentx and sets the espectations for the app, I do think that this is necesary only when we dont dectect the face or the human (or dog) and it should be ask in a nice or funny way (Are you sure there are humans on this photo... the robots in this app need to see 2 eyes and a mouth to figuere it out correctly) this way its relevant to that moment and you could have a reteke picture or smthing button

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 [18]:
## (Optional) TODO: Report the performance of another  
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.

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 [19]:
from keras.applications.resnet50 import ResNet50

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')

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 [20]:
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 [21]:
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 [22]:
### 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: Here we have a 100% acurete prediction, 0% of human pics where dogs and 100% of dogs images are dogs


In [23]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.

humans_dogs = [path for path in human_files_short if dog_detector(path)]
dogs_dogs = [path for path in dog_files_short if dog_detector(path)]


print('There are %d out of %d where detected human faces the %.1f%%' %(len(humans_dogs),len(human_files_short),(100* len(humans_dogs)/len(human_files_short))))
print('There are %d out of %d where detected dogs the %.1f%%' %(len(dogs_dogs),len(dog_files_short),(100* len(dogs_dogs)/len(dog_files_short))))


There are 0 out of 100 where detected human faces the 0.0%
There are 100 out of 100 where detected dogs the 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 [24]:
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 [00:59<00:00, 112.70it/s]
100%|██████████| 835/835 [00:06<00:00, 123.80it/s]
100%|██████████| 836/836 [00:06<00:00, 124.67it/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:

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: I use the proposed CNN model architectire, first and far most because it has all the elements or layers that where shown in the lecture , convolutional follow by MaxPolling and this was done a couple of times (a VGG3 type) finishing with the average pooling that will basicaly flattend the result enogh to aplay the fully conected sofmax layer that will actualy predict the outcome, and in the other hand is small enogh that it will work as a base line for the next part of the project (and sure enogh the results are overwerlming comere to the transfer learnig were weget more than a 10X performences acuraecy 4% to 40% vs VGG16 and 20X compare to Inseption the one I choses gave a wopping 82%)


In [25]:
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.

dog_class = len(dog_names)

model.add(Conv2D(filters=16, kernel_size=2, strides=(1,1), padding='valid', activation='relu', input_shape=(224,224,3)))
model.add(MaxPooling2D(pool_size=2, strides=(2,2)))

model.add(Conv2D(filters=32, kernel_size=2, strides=(1,1), padding='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=2, strides=(2,2)))

model.add(Conv2D(filters=64, kernel_size=2, strides=(1,1), padding='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=2, strides=(2,2)))

model.add(GlobalAveragePooling2D())
model.add(Dense(dog_class, activation="softmax"))


model.summary()


_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 223, 223, 16)      208       
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 111, 111, 16)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 110, 110, 32)      2080      
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 55, 55, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 54, 54, 64)        8256      
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 27, 27, 64)        0         
_________________________________________________________________
global_average_pooling2d_1 ( (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 133)               8645      
=================================================================
Total params: 19,189
Trainable params: 19,189
Non-trainable params: 0
_________________________________________________________________

Compile the Model


In [26]:
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 [27]:
from keras.callbacks import ModelCheckpoint  

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

epochs = 15

### 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 1/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.8833 - acc: 0.0086Epoch 00000: val_loss improved from inf to 4.86369, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 34s - loss: 4.8832 - acc: 0.0085 - val_loss: 4.8637 - val_acc: 0.0108
Epoch 2/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.8540 - acc: 0.0128Epoch 00001: val_loss improved from 4.86369 to 4.83720, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.8541 - acc: 0.0127 - val_loss: 4.8372 - val_acc: 0.0168
Epoch 3/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.8099 - acc: 0.0176Epoch 00002: val_loss improved from 4.83720 to 4.80443, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.8101 - acc: 0.0175 - val_loss: 4.8044 - val_acc: 0.0144
Epoch 4/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.7780 - acc: 0.0186Epoch 00003: val_loss improved from 4.80443 to 4.79402, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.7777 - acc: 0.0187 - val_loss: 4.7940 - val_acc: 0.0216
Epoch 5/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.7521 - acc: 0.0192Epoch 00004: val_loss improved from 4.79402 to 4.77093, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.7518 - acc: 0.0192 - val_loss: 4.7709 - val_acc: 0.0192
Epoch 6/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.7306 - acc: 0.0200Epoch 00005: val_loss improved from 4.77093 to 4.75419, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.7313 - acc: 0.0201 - val_loss: 4.7542 - val_acc: 0.0216
Epoch 7/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.7091 - acc: 0.0297Epoch 00006: val_loss did not improve
6680/6680 [==============================] - 33s - loss: 4.7085 - acc: 0.0298 - val_loss: 4.7582 - val_acc: 0.0299
Epoch 8/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.6881 - acc: 0.0275Epoch 00007: val_loss improved from 4.75419 to 4.73227, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.6871 - acc: 0.0278 - val_loss: 4.7323 - val_acc: 0.0287
Epoch 9/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.6618 - acc: 0.0335Epoch 00008: val_loss improved from 4.73227 to 4.71094, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.6623 - acc: 0.0334 - val_loss: 4.7109 - val_acc: 0.0299
Epoch 10/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.6419 - acc: 0.0345Epoch 00009: val_loss improved from 4.71094 to 4.70510, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.6430 - acc: 0.0344 - val_loss: 4.7051 - val_acc: 0.0359
Epoch 11/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.6269 - acc: 0.0362Epoch 00010: val_loss improved from 4.70510 to 4.67886, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.6270 - acc: 0.0361 - val_loss: 4.6789 - val_acc: 0.0275
Epoch 12/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.6100 - acc: 0.0378Epoch 00011: val_loss improved from 4.67886 to 4.66467, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.6106 - acc: 0.0379 - val_loss: 4.6647 - val_acc: 0.0287
Epoch 13/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.5944 - acc: 0.0384Epoch 00012: val_loss improved from 4.66467 to 4.66131, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.5942 - acc: 0.0383 - val_loss: 4.6613 - val_acc: 0.0228
Epoch 14/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.5783 - acc: 0.0407Epoch 00013: val_loss improved from 4.66131 to 4.65335, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.5786 - acc: 0.0406 - val_loss: 4.6534 - val_acc: 0.0347
Epoch 15/15
6660/6680 [============================>.] - ETA: 0s - loss: 4.5581 - acc: 0.0452Epoch 00014: val_loss improved from 4.65335 to 4.63638, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 33s - loss: 4.5582 - acc: 0.0452 - val_loss: 4.6364 - val_acc: 0.0299
Out[27]:
<keras.callbacks.History at 0x7f0a94654a90>

Load the Model with the Best Validation Loss


In [29]:
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 [30]:
# 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: 4.6651%

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 [32]:
bottleneck_features = np.load('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 [33]:
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_4 ( (None, 512)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 133)               68229     
=================================================================
Total params: 68,229
Trainable params: 68,229
Non-trainable params: 0
_________________________________________________________________

Compile the Model


In [34]:
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

Train the Model


In [35]:
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
6500/6680 [============================>.] - ETA: 0s - loss: 12.2788 - acc: 0.1258Epoch 00000: val_loss improved from inf to 10.75314, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 12.2268 - acc: 0.1290 - val_loss: 10.7531 - val_acc: 0.1964
Epoch 2/20
6500/6680 [============================>.] - ETA: 0s - loss: 10.1695 - acc: 0.2798Epoch 00001: val_loss improved from 10.75314 to 10.13408, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 10.1942 - acc: 0.2781 - val_loss: 10.1341 - val_acc: 0.2814
Epoch 3/20
6500/6680 [============================>.] - ETA: 0s - loss: 9.6766 - acc: 0.3408Epoch 00002: val_loss improved from 10.13408 to 9.75311, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 9.6777 - acc: 0.3406 - val_loss: 9.7531 - val_acc: 0.3210
Epoch 4/20
6480/6680 [============================>.] - ETA: 0s - loss: 9.4156 - acc: 0.3713Epoch 00003: val_loss improved from 9.75311 to 9.71671, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 9.4047 - acc: 0.3717 - val_loss: 9.7167 - val_acc: 0.3210
Epoch 5/20
6500/6680 [============================>.] - ETA: 0s - loss: 9.2316 - acc: 0.3946Epoch 00004: val_loss improved from 9.71671 to 9.70762, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 9.2534 - acc: 0.3933 - val_loss: 9.7076 - val_acc: 0.3269
Epoch 6/20
6500/6680 [============================>.] - ETA: 0s - loss: 9.1145 - acc: 0.4066Epoch 00005: val_loss improved from 9.70762 to 9.68835, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 9.1287 - acc: 0.4061 - val_loss: 9.6883 - val_acc: 0.3186
Epoch 7/20
6500/6680 [============================>.] - ETA: 0s - loss: 9.0235 - acc: 0.4202Epoch 00006: val_loss improved from 9.68835 to 9.53708, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 9.0390 - acc: 0.4186 - val_loss: 9.5371 - val_acc: 0.3425
Epoch 8/20
6480/6680 [============================>.] - ETA: 0s - loss: 8.9162 - acc: 0.4302Epoch 00007: val_loss improved from 9.53708 to 9.40536, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.9045 - acc: 0.4302 - val_loss: 9.4054 - val_acc: 0.3509
Epoch 9/20
6500/6680 [============================>.] - ETA: 0s - loss: 8.7391 - acc: 0.4394Epoch 00008: val_loss improved from 9.40536 to 9.18778, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.7574 - acc: 0.4386 - val_loss: 9.1878 - val_acc: 0.3701
Epoch 10/20
6500/6680 [============================>.] - ETA: 0s - loss: 8.5331 - acc: 0.4538Epoch 00009: val_loss improved from 9.18778 to 9.06934, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.5547 - acc: 0.4522 - val_loss: 9.0693 - val_acc: 0.3677
Epoch 11/20
6500/6680 [============================>.] - ETA: 0s - loss: 8.4434 - acc: 0.4662Epoch 00010: val_loss improved from 9.06934 to 8.96883, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.4236 - acc: 0.4672 - val_loss: 8.9688 - val_acc: 0.3796
Epoch 12/20
6500/6680 [============================>.] - ETA: 0s - loss: 8.3824 - acc: 0.4698Epoch 00011: val_loss improved from 8.96883 to 8.96426, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.3796 - acc: 0.4696 - val_loss: 8.9643 - val_acc: 0.3689
Epoch 13/20
6500/6680 [============================>.] - ETA: 0s - loss: 8.2737 - acc: 0.4749Epoch 00012: val_loss improved from 8.96426 to 8.91477, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.2424 - acc: 0.4768 - val_loss: 8.9148 - val_acc: 0.3808
Epoch 14/20
6500/6680 [============================>.] - ETA: 0s - loss: 8.1852 - acc: 0.4845Epoch 00013: val_loss improved from 8.91477 to 8.72199, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.1975 - acc: 0.4826 - val_loss: 8.7220 - val_acc: 0.3832
Epoch 15/20
6500/6680 [============================>.] - ETA: 0s - loss: 7.9252 - acc: 0.4906Epoch 00014: val_loss improved from 8.72199 to 8.44055, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.9168 - acc: 0.4910 - val_loss: 8.4406 - val_acc: 0.4036
Epoch 16/20
6500/6680 [============================>.] - ETA: 0s - loss: 7.7040 - acc: 0.5069Epoch 00015: val_loss improved from 8.44055 to 8.33811, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.7335 - acc: 0.5054 - val_loss: 8.3381 - val_acc: 0.4180
Epoch 17/20
6500/6680 [============================>.] - ETA: 0s - loss: 7.6721 - acc: 0.5143Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 1s - loss: 7.6766 - acc: 0.5141 - val_loss: 8.3432 - val_acc: 0.4156
Epoch 18/20
6500/6680 [============================>.] - ETA: 0s - loss: 7.6196 - acc: 0.5174Epoch 00017: val_loss improved from 8.33811 to 8.33631, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.6137 - acc: 0.5177 - val_loss: 8.3363 - val_acc: 0.4216
Epoch 19/20
6500/6680 [============================>.] - ETA: 0s - loss: 7.5164 - acc: 0.5249Epoch 00018: val_loss improved from 8.33631 to 8.26443, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.5364 - acc: 0.5238 - val_loss: 8.2644 - val_acc: 0.4240
Epoch 20/20
6500/6680 [============================>.] - ETA: 0s - loss: 7.4514 - acc: 0.5295Epoch 00019: val_loss improved from 8.26443 to 8.26370, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.4676 - acc: 0.5284 - val_loss: 8.2637 - val_acc: 0.4192
Out[35]:
<keras.callbacks.History at 0x7f88c464cb00>

Load the Model with the Best Validation Loss


In [36]:
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 [37]:
# 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: 43.4211%

Predict Dog Breed with the Model


In [38]:
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:

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. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.

(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('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']

In [29]:
### TODO: Obtain bottleneck features from another pre-trained CNN.
bottleneck_features = np.load('bottleneck_features/DogInceptionV3Data.npz')
train_Inception = bottleneck_features['train']
valid_Inception = bottleneck_features['valid']
test_Inception = 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: After looking arround I found out that inception is google open images algorithim and that is one of the best that can be minimize or srunken with imagesnet or Alexnet https://hackernoon.com/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3 this is important to me bucause i want to create a android app that helps autistic kids learn to talk so its a step in that direction, for the rest step I implemented a similar architecture as the previews point:

Made a Sequential model passing the InceptionV3 Bottleneck firt in a Global Average Pooling and then just a Dennse 133 nodes (one for each dog bread) with a softmax activation function that will give the final prediction

(IMPLEMENTATION) Compile the Model


In [31]:
### TODO: Compile the model.
model_inc.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 [30]:
### TODO: Define your architecture.
#Create the inception model 
model_inc = Sequential()

#add the pooling layer with the imput from the bottleneck
model_inc.add(GlobalAveragePooling2D(input_shape=train_Inception.shape[1:]))

## add a Dense layer with softmax activation to get the prediction 
model_inc.add(Dense(133, activation='softmax'))

model_inc.summary()


_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_2 ( (None, 2048)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 133)               272517    
=================================================================
Total params: 272,517
Trainable params: 272,517
Non-trainable params: 0
_________________________________________________________________

In [32]:
### TODO: Train the model.
my_checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.InceptionV3.hdf5', verbose=1, save_best_only=True)
model_inc.fit(train_Inception, train_targets, validation_data=(valid_Inception, valid_targets), epochs=20, batch_size=30, callbacks=[my_checkpointer], verbose=1)


Train on 6680 samples, validate on 835 samples
Epoch 1/20
6630/6680 [============================>.] - ETA: 0s - loss: 1.2003 - acc: 0.6980Epoch 00000: val_loss improved from inf to 0.65466, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 3s - loss: 1.2016 - acc: 0.6981 - val_loss: 0.6547 - val_acc: 0.7904
Epoch 2/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.4629 - acc: 0.8552Epoch 00001: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.4640 - acc: 0.8546 - val_loss: 0.6558 - val_acc: 0.8192
Epoch 3/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.3303 - acc: 0.8940Epoch 00002: val_loss improved from 0.65466 to 0.60650, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 2s - loss: 0.3300 - acc: 0.8943 - val_loss: 0.6065 - val_acc: 0.8455
Epoch 4/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.2660 - acc: 0.9175Epoch 00003: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.2653 - acc: 0.9178 - val_loss: 0.6161 - val_acc: 0.8491
Epoch 5/20
6600/6680 [============================>.] - ETA: 0s - loss: 0.2089 - acc: 0.9306Epoch 00004: val_loss improved from 0.60650 to 0.58693, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 2s - loss: 0.2097 - acc: 0.9307 - val_loss: 0.5869 - val_acc: 0.8563
Epoch 6/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.1683 - acc: 0.9448Epoch 00005: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.1682 - acc: 0.9448 - val_loss: 0.6801 - val_acc: 0.8479
Epoch 7/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.1402 - acc: 0.9529Epoch 00006: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.1394 - acc: 0.9533 - val_loss: 0.6812 - val_acc: 0.8611
Epoch 8/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.1137 - acc: 0.9605Epoch 00007: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.1141 - acc: 0.9605 - val_loss: 0.7462 - val_acc: 0.8503
Epoch 9/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0933 - acc: 0.9719Epoch 00008: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0940 - acc: 0.9716 - val_loss: 0.6806 - val_acc: 0.8635
Epoch 10/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0828 - acc: 0.9742Epoch 00009: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0830 - acc: 0.9741 - val_loss: 0.7585 - val_acc: 0.8407
Epoch 11/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0685 - acc: 0.9784Epoch 00010: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0693 - acc: 0.9784 - val_loss: 0.7407 - val_acc: 0.8563
Epoch 12/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0595 - acc: 0.9808Epoch 00011: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0597 - acc: 0.9807 - val_loss: 0.7669 - val_acc: 0.8503
Epoch 13/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0483 - acc: 0.9851Epoch 00012: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0490 - acc: 0.9849 - val_loss: 0.8134 - val_acc: 0.8455
Epoch 14/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0419 - acc: 0.9875Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0423 - acc: 0.9873 - val_loss: 0.8363 - val_acc: 0.8467
Epoch 15/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0362 - acc: 0.9888Epoch 00014: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0360 - acc: 0.9889 - val_loss: 0.8060 - val_acc: 0.8443
Epoch 16/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0329 - acc: 0.9899Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0328 - acc: 0.9900 - val_loss: 0.8302 - val_acc: 0.8515
Epoch 17/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0303 - acc: 0.9902Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0301 - acc: 0.9903 - val_loss: 0.8033 - val_acc: 0.8587
Epoch 18/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0259 - acc: 0.9926Epoch 00017: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0257 - acc: 0.9927 - val_loss: 0.8554 - val_acc: 0.8527
Epoch 19/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0221 - acc: 0.9928Epoch 00018: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0220 - acc: 0.9928 - val_loss: 0.8249 - val_acc: 0.8599
Epoch 20/20
6630/6680 [============================>.] - ETA: 0s - loss: 0.0186 - acc: 0.9953Epoch 00019: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0185 - acc: 0.9954 - val_loss: 0.8721 - val_acc: 0.8527
Out[32]:
<keras.callbacks.History at 0x7f0a94408f28>

(IMPLEMENTATION) Load the Model with the Best Validation Loss


In [33]:
### TODO: Load the model weights with the best validation loss.
model_inc.load_weights('saved_models/weights.best.InceptionV3.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 [34]:
### TODO: Calculate classification accuracy on the test dataset.
InceptionV3Preditction = [np.argmax(model_inc.predict(np.expand_dims(feature, axis=0))) for feature in test_Inception]
test_accuracy = 100*np.sum(np.array(InceptionV3Preditction)==np.argmax(test_targets, axis=1))/len(InceptionV3Preditction)
print('Test accuracy: %.4f%%' % test_accuracy)


Test accuracy: 82.2967%

(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 [40]:
### 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 extract_InceptionV3
def InceptionV3_predict_breed(img_path):
    #Extract features from Resnet using the prebuilt functions
    feature = extract_InceptionV3(path_to_tensor(img_path))
    #Make a prediction
    prediction = model_inc.predict(feature)
    #Find and return the corresponding dog breed
    dogbreed_index = np.argmax(prediction)
    return dog_names[dogbreed_index]

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!

(IMPLEMENTATION) Write your Algorithm


In [69]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
from IPython.display import display, Image

def my_algorithm(img_path):
    print("AI Robot looking at img...> "+img_path)
    display(Image(filename=img_path))
   
    if dog_detector(img_path):
        Messages = "AI Robot has detected This is dog bread is...: "
       
    elif face_detector(img_path):
        Messages = "AI Robot thinks this is a human that looks some what as a dog .. "     
    else:
        Messages = "Nor dogs nor human but if it where robot think it would be a .. "
    #We Print the messages and the prediction
    prediction = InceptionV3_predict_breed(img_path) 
    print(Messages, prediction)

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: The output was good but I though it would be better, specially the human detection, one of my photos was a bit blur and it miss the human completly, but in general im happy to have gotten to this point all the dogs where detected dogs at least and most of them where correctly classified, but in did my pics wherent normalizes and like I said some where a bit blurie

To make it better first I would implement images augmentation (rotating and rescaling images) Also test with other trasfer learning algorith like Resnet50 and VGG19 And try out somo other activation funtions and model parameters, epohcs, and bath sizes and create my own bottelneck adding more testing samples


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## 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.

## We create a list for the testing personal pics all uploaded in the images/ folder
test_pics = ['aaabbb.png','ASDDDD1.png','BB22WW.png','CCC22WW.png','DSC00566.JPG','DSC00569.JPG','DSC_0440.JPG','SS222WWW.png']

test_ai = [pic for pic in test_pics if my_algorithm('images/'+pic)]


AI Robot looking at img...> images/aaabbb.png
AI Robot thinks this is a human that looks some what as a dog ..  Dachshund
AI Robot looking at img...> images/ASDDDD1.png
AI Robot thinks this is a human that looks some what as a dog ..  Dachshund
AI Robot looking at img...> images/BB22WW.png
AI Robot has detected This is dog bread is...:  German_shepherd_dog
AI Robot looking at img...> images/CCC22WW.png
AI Robot has detected This is dog bread is...:  Great_pyrenees
AI Robot looking at img...> images/DSC00566.JPG
AI Robot thinks this is a human that looks some what as a dog ..  Greyhound
AI Robot looking at img...> images/DSC00569.JPG
Nor dogs nor human but if it where robot think it would be a ..  Mastiff
AI Robot looking at img...> images/DSC_0440.JPG
IOPub data rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_data_rate_limit`.
AI Robot thinks this is a human that looks some what as a dog ..  Chinese_crested
AI Robot looking at img...> images/SS222WWW.png
AI Robot has detected This is dog bread is...:  Golden_retriever

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