Convolutional Neural Networks

---

### Convolutional Neural Networks

### Applications of Data Science - Class 17

### Giora Simchoni

#### `gsimchoni@gmail.com and add #dsapps in subject`

### Stat. and OR Department, TAU
### 2020-06-18

---

<div class="my-footer">
  <span>
    <a href="https://dsapps-2020.github.io/Class_Slides/" target="_blank">Applications of Data Science
    </a>
  </span>
</div>

---

# Understanding Images

---

```python
import matplotlib.pyplot as plt
from matplotlib.image import imread

logo = imread('../DSApps_logo.jpg')

plt.imshow(logo)
plt.show()
```

---

```python
print(type(logo))
```

```
## <class 'numpy.ndarray'>
```

```python
print(logo.shape)
```

```
## (1400, 1400, 3)
```

```python
print(logo[:4, :4, 0])
```

```
## [[6 6 6 6]
##  [6 6 6 6]
##  [6 6 6 6]
##  [6 6 6 6]]
```

```python
print(logo.min(), logo.max())
```

```
## 0 255
```

```python
print(logo.dtype, logo.size * logo.itemsize)
```

```
## uint8 5880000
```

---

### Red channel

```python
red = logo[:, :, 0]
plt.imshow(red,
  cmap='gray')
plt.show()
```

]

```python
fig = plt.hist(red.flatten(),
  color = 'r')
fig = plt.ylim(0, 1700000)
plt.show()
```

]

---

### Green channel

```python
green = logo[:, :, 1]
plt.imshow(green,
  cmap='gray')
plt.show()
```

]

```python
fig = plt.hist(green.flatten(),
  color = 'g')
fig = plt.ylim(0, 1700000)
plt.show()
```

]

---

### Blue channel

```python
blue = logo[:, :, 2]
plt.imshow(blue,
  cmap='gray')
plt.show()
```

]

```python
fig = plt.hist(blue.flatten(),
  color = 'b')
fig = plt.ylim(0, 1700000)
plt.show()
```

]

---

### Until Convolutional Networks

- In 1998 LeCun et. al. published [LeNet-5](https://ieeexplore.ieee.org/abstract/document/726791) for digit recognition
- But it wasn't until 2012 when Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton published [AlexNet](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) when the Deep Learning mania really took off.

Until then:

1. Treat it as a regular high-dimensional ML problem
2. Image feature engineering

---

# Convolutional Layer (2D)

---

### What is Convolution?

---

### The Convolutional Layer

.font80percent[Source: [Geron 2019](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)]

- First layer: every neuron has a "receptive field", it is focused on a specific rectangle of the image, usually 2x2, 3x3
- Second layer: every neuron has a receptive field in the first layer
- Etc.

---

### More specifically

.font80percent[Source: [Geron 2019](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)]

- The `\([i,j]\)` neuron looks at the rectangle at rows `\(i\)` to `\(i + f_h - 1\)`, columns `\(j\)` to `\(j + f_w - 1\)`
- Zero Padding: adds 0s around the image to make the next layer the same dimensions

---

### Filters/Features/Kernels

But what does the neuron actually *do*?

All neurons in a layer learn a single *filter* the size of their receptive field, the `\(f_h, f_w\)` rectangle.

Suppose `\(f_h=f_w=3\)` and the first layer learned the `\(W_{3x3}\)` filter:

```python
W = np.array(
  [
    [0, 1, 0],
    [0, 1, 0],
    [0, 1, 0]
  ]
)
```

---

`\(X\)` is the 5x5 image, suppose it has a single color channel (i.e. grayscale), sort of a smiley:

```python
X = np.array(
  [
    [0,   1,  0,  1,  0],
    [0,   1,  0,  1,  0],
    [0,   0,  0,  0,  0],
    [1,   0,  0,  0,  1],
    [0,   1,  1,  1,  0]
  ]
)
```

Each nuron in the new layer `\(Z\)` would be the sum of elementwise multiplication of all 3x3 pixels/neurons in its receptive field with `\(W_{3x3}\)`:

`\(Z_{i,j} = b + \sum_{u=0}^{f_h-1}\sum_{v=0}^{f_w-1}X_{i+u,j+v}W_{u,v}\)`

---

### Good God, Excel?

.insight[
💡 What low-level feature did this layer learn to look for? What pattern will make its neurons most positive (i.e. will "turn them on")?
]

---

### Another filter

.insight[
💡 What low-level feature did this layer learn to look for? What pattern will make its neurons most positive (i.e. will "turn them on")?
]

---

### Convolving with Tensorflow

```python
import tensorflow as tf

ny = imread('images/new_york.jpg').mean(axis=2)
ny4D = np.array([ny.reshape(ny.shape[0], ny.shape[1], 1)])
W4d = W.reshape(W.shape[0], W.shape[0], 1, 1)

ny_convolved = tf.nn.conv2d(ny4D, W4d, strides=1, padding='SAME')
```

---

```python
plt.imshow(
  ny,
  cmap = 'gray')
plt.show()
```

]

```python
plt.imshow(
  ny_convolved[0, :, :, 0],
  cmap = 'gray')
plt.show()
```

]

---

### Strides

In many CNN architechtures layers tend to get smaller and smaller using *strides*:

- The `\([i,j]\)` neuron looks at the rectangle at rows `\(i \cdot s_h\)` to `\(i \cdot s_h + f_h - 1\)`, columns `\(j \cdot s_w\)` to `\(j \cdot s_w + f_w - 1\)`

---

### Stacking Feature Maps

A convolutional layer is actually a 3D stack of a few of the 2D layers we described (a.k.a *Feature Maps*), each learns a single filter.

.font80percent[Source: [Geron 2019](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)]

---

- Feature map 1 learns horizontal lines
- Feature map 2 learns vertical lines
- Feature map 3 learns diagonal lines
- Etc.

And each such feature map takes as inputs all feature maps (or color channels) in the previous layer, and sums:

`\(Z_{i,j,k} = b_k + \sum_{u=0}^{f_h-1}\sum_{v=0}^{f_w-1}\sum_{k'=0}^{f_n-1}X_{i \cdot s_h+u,j \cdot s_w+v, k'} \cdot W_{u,v,k', k}\)`

Where `\(f_n\)` is the number of feature maps (or color channels) in the previous layer.

- Feature map 1 with `\(f_h \cdot f_w\)` filter `\(\cdot f_n\)` color channels + 1 bias term (it's a filter *cube*!)
- Feature map 2 with `\(f_h \cdot f_w\)` filter `\(\cdot f_n\)` color channels + 1 bias term
- Etc.

---

### And how does the network *learn* these filters?

---

### Too early to rejoice

That's still quite a lot.

- 100 x 100 RGB image
- 100 feature maps in first layer of filter 3x3
- (3 x 3 x 3 + 1) x 100 = 2800 params, not too bad
- But 100 x 100 numbers in each feature map (x 100) = 1M numbers, each say takes 4B, that's 4MB for 1 image for 1 layer
- Each number is a weighted sum of 3 x 3 x 3 = 27 numbers, so that's 1M x 27 = 27M multiplications for 1 layer...

---

### Pooling Layers

A pooling layer "sums up" a convolutional layer, by taking the max or mean of the receptive field.

No params!

.font80percent[Source: [Geron 2019](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)]

---

Usually with strides `\(s_w=s_h=f_w=f_h\)` and no padding (a.k.a `VALID`):

---

Clearly we're losing info.

.insight[
💡 A 2x2 max pooling layer would reduce the size of the previous layer by how many percents?
]

But maybe sometimes it's a *good* thing to ignore some neurons? Where did we get this crazy idea from?

---

### Image Data Augmentation

It's very hard to "augment" data about people, products, clicks.

Images are different.

- Translation
- Rotation
- Rescaling
- Flipping
- Stretching

.insight[
💡 Are there some images or applications you wouldn't want augmentations or at least should go about it very carefully?
]

---

In Keras you can augment "on the fly" as you train your model, here I'm just showing you this works:

```python
from tensorflow.keras.preprocessing.image import ImageDataGenerator

idg = ImageDataGenerator(
	rotation_range=30,
	zoom_range=0.15,
	width_shift_range=0.2,
	height_shift_range=0.2,
	shear_range=0.15,
	horizontal_flip=True,
	fill_mode='nearest'
)

ny_generator = idg.flow(ny4D, batch_size=1)

nys = [ny_generator.next() for i in range(10)]
```

See the [ImageDataGenerator](https://keras.io/api/preprocessing/image/) for more.

---

---

### Why do CNN work?

- No longer a long 1D column of neurons, but 2D, taking into accout spatial realtions between pixels/neurons
- First layer learns very low-level features, second layer learns higher level features, etc.
- Shared weights --> learn feature in one area of the image, generalize it to the entire image
- Less weights --> smaller size, more feasible model, less prone to overfitting
- Less overfitting by Pooling
- Invariance by Max Pooling
- Hardware/Software advancements enable processing of huge amounts of images

---

# Back to Malaria!

---

```python
import tensorflow_datasets as tfds
from skimage.transform import resize
from sklearn.model_selection import train_test_split

malaria, info = tfds.load('malaria', split='train', with_info=True)

images = []
labels = []
for example in tfds.as_numpy(malaria):
  images.append(resize(example['image'], (100, 100)).astype(np.float32))
  labels.append(example['label'])
  if len(images) == 2500:
    break
  
X = np.array(images)
y = np.array(labels)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

print(X_train.shape)
```

```
## (2000, 100, 100, 3)
```

```python
print(X_test.shape)
```

```
## (500, 100, 100, 3)
```

---

```python
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout
from tensorflow.keras.callbacks import EarlyStopping

model = Sequential()
model.add(Conv2D(filters=32, input_shape=(X_train.shape[1:]),
  kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
  optimizer='adam', metrics=['accuracy'])
```

---

```python
model.summary()
```

```
## Model: "sequential"
## _________________________________________________________________
## Layer (type)                 Output Shape              Param #   
## =================================================================
## conv2d (Conv2D)              (None, 100, 100, 32)      896       
## _________________________________________________________________
## max_pooling2d (MaxPooling2D) (None, 50, 50, 32)        0         
## _________________________________________________________________
## conv2d_1 (Conv2D)            (None, 48, 48, 64)        18496     
## _________________________________________________________________
## max_pooling2d_1 (MaxPooling2 (None, 24, 24, 64)        0         
## _________________________________________________________________
## flatten (Flatten)            (None, 36864)             0         
## _________________________________________________________________
## dropout (Dropout)            (None, 36864)             0         
## _________________________________________________________________
## dense (Dense)                (None, 1)                 36865     
## =================================================================
## Total params: 56,257
## Trainable params: 56,257
## Non-trainable params: 0
## _________________________________________________________________
```

---

```python
from tensorflow.keras import utils

utils.plot_model(model, 'images/malaria_cnn.png', show_shapes=True)
```

---

```python
callbacks = [EarlyStopping(monitor='val_loss', patience=5)]
history = model.fit(X_train, y_train, batch_size=100, epochs=50,
  validation_split=0.1, callbacks=callbacks)

# Epoch 1/50
# 
#  1/18 [>.............................] - ETA: 0s - loss: 0.6919 - accuracy: 0.5000
#  2/18 [==>...........................] - ETA: 8s - loss: 0.6895 - accuracy: 0.5600
#  3/18 [====>.........................] - ETA: 11s - loss: 0.7002 - accuracy: 0.5433
#  4/18 [=====>........................] - ETA: 11s - loss: 0.7009 - accuracy: 0.5275
#  5/18 [=======>......................] - ETA: 11s - loss: 0.6954 - accuracy: 0.5420
#  6/18 [=========>....................] - ETA: 10s - loss: 0.6925 - accuracy: 0.5550
#  7/18 [==========>...................] - ETA: 9s - loss: 0.6882 - accuracy: 0.5714 
```

---

```python
import pandas as pd

pd.read_csv(history.history).plot()
plt.grid(True)
plt.show()
```

---

### Last time we got to 66% accuracy after tuning...

```python
from sklearn.metrics import confusion_matrix

y_pred = (model.predict(X_test) > 0.5).astype(int).reshape(y_test.shape)
```

```python
np.mean(y_pred == y_test)
```

```
## 0.766
```

```python
pd.DataFrame(
  confusion_matrix(y_test, y_pred), 
  index=['true:yes', 'true:no'], 
  columns=['pred:yes', 'pred:no']
)
```

```
##           pred:yes  pred:no
## true:yes       191       71
## true:no         46      192
```

---

### And this is just 10% of the data.

Watch me reach ~94% accuracy with 100% of the data in [Google Colab](https://colab.research.google.com/drive/1i39eWL8e5Gl3rjoulh5WJMVOUgvM8h6_?usp=sharing).

---

### Visualizing filters/kernels/features/weights

```python
W, b = model.get_layer('conv2d').get_weights()
```

```python
W.shape
```

```
## (3, 3, 3, 32)
```

```python
W[:, :, 0, 0]
```

```
## array([[-0.05327014,  0.05076471,  0.0883847 ],
##        [-0.0933094 ,  0.14134812, -0.08010183],
##        [ 0.08545554,  0.10305361,  0.14380825]], dtype=float32)
```

---

First filter of 32:

```python
plt.subplot(1,3,1)
plt.imshow(W[:, :, 0, 0], cmap='gray')
plt.subplot(1,3,2)
plt.imshow(W[:, :, 1, 0], cmap='gray')
plt.subplot(1,3,3)
plt.imshow(W[:, :, 2, 0], cmap='gray')
plt.show()
```

---

### Visualizing Feature Maps

```python
cell = X_test[0, :, :, :].reshape(1, 100, 100, 3)

plt.imshow(cell[0])
plt.show()
```

---

```python
from tensorflow.keras import Model

model_1layer = Model(inputs=model.inputs,
  outputs=model.layers[0].output)

feature_maps = model_1layer.predict(cell)

feature_maps.shape
```

```
## (1, 100, 100, 32)
```

```python
# source: https://machinelearningmastery.com/how-to-visualize-filters-and-feature-maps-in-convolutional-neural-networks/
width = 8
height = 4
ix = 1
for _ in range(height):
	for _ in range(width):
		ax = plt.subplot(height, width, ix)
		_ = ax.set_xticks([])
		_ = ax.set_yticks([])
		_ = plt.imshow(feature_maps[0, :, :, ix-1], cmap='gray')
		ix += 1
plt.show()
```

---

---

# CNN Architectures

---

### ImageNet

.font80percent[Source: [paperswithcode.com](https://paperswithcode.com/sota/image-classification-on-imagenet)]

---

### LeNet-5 (1998)

.font80percent[Source: [LeCun et. al. 1998](http://www.dengfanxin.cn/wp-content/uploads/2016/03/1998Lecun.pdf)]

You know you can implement this in just a few lines of Keras...

---

```python
from tensorflow.keras.layers import Conv2D, AveragePooling2D,
  Flatten, Dense
from tensorflow.keras import Sequential

model = keras.Sequential()

model.add(Conv2D(filters=6, kernel_size=(3, 3),
  activation='relu', input_shape=(32,32,1)))
model.add(AveragePooling2D())
model.add(Conv2D(16, kernel_size=(3, 3), activation='relu'))
model.add(AveragePooling2D())
model.add(Flatten())
model.add(Dense(120, activation='relu'))
model.add(Dense(84, activation='relu'))
model.add(Dense(10, activation = 'softmax'))
```

---

### AlexNet (2012)

.font80percent[Source: [Krizhevsky et. al. 2012](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)]

See a Keras implementation e.g. [here](https://engmrk.com/alexnet-implementation-using-keras/)

---

### ResNet (2015)

]

- Source: [He et. al.](https://arxiv.org/pdf/1512.03385.pdf)
- This is ResNet-34 (34 layers)
- ResNet-152 (152 layers!) achieved 4.5% To-5 error rate with single model

]

---

#### Residual Learning

- "Is learning better networks as easy as stacking more layers?" Yes, but:
- "When deeper networks are able to start converging, a degradation problem has been exposed: with the network depth increasing, accuracy gets saturated... and then degrades rapidly. Unexpectedly, such degradation is not caused by overfitting, and adding more layers to a suitably deep model leads to higher training error" (a.k.a the vanishing/exploding gradients problem)
- With *Residual Learning* the activation from a previous layer is being added to the activation of a deeper layer in the network
- The signal is allowed to propagate through the layers

.insight[
💡 The model learning `\(H(X)\)` will be forced to learn `\(H(X) - X\)`, hence *residual* learning.
]

---

# Using Pre-trained Models

---

### Why are we working so hard?

Look up [keras.io/api/applications/](https://keras.io/api/applications/)

---

```python
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
from skimage.transform import resize

model = ResNet50(weights='imagenet')

johann = imread('images/johann.jpg')

fig = plt.imshow(johann)
plt.show()
```

---

```python
johann = resize(johann, (224, 224))
johann = johann.reshape(1, 224, 224, 3) * 255
johann = preprocess_input(johann)

johann_pred = model.predict(johann)

print(johann_pred.shape)
```

```
## (1, 1000)
```

```python
johann_breed = decode_predictions(johann_pred, top=5)

for _, breed, score in johann_breed[0]:
  print('%s: %.2f' % (breed, score))
```

```
## Norwegian_elkhound: 0.30
## Eskimo_dog: 0.28
## Siberian_husky: 0.17
## German_shepherd: 0.14
## malamute: 0.06
```

---

# Transfer Learning

---

### Got Data?

- I want a model that would be able to tell a barking dog vs. a non-barking dog.
- I have only 100 images of barking dogs and 100 images of non-barking dogs, I lazily saved from Google Images.
- I use `ImageDataGenerator` to augment my training set and build my go-to CNN.

---

```python
from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
  rotation_range = 30,
	zoom_range = 0.15,
	width_shift_range = 0.2,
	height_shift_range = 0.2,
	shear_range = 0.15,
	horizontal_flip = True,
	fill_mode = 'nearest'
)

valid_datagen = ImageDataGenerator()
test_datagen = ImageDataGenerator()
```

```python
train_generator = train_datagen.flow_from_directory(
  'dogs/train/',
  target_size = (224,224),
  color_mode = 'rgb',
  batch_size = 10,
  class_mode = 'binary',
  shuffle = True,
  seed = 42
)
```

```
## Found 120 images belonging to 2 classes.
```

---

```python
valid_generator = valid_datagen.flow_from_directory(
  'dogs/valid/',
  target_size = (224,224),
  color_mode = 'rgb',
  batch_size = 10,
  class_mode = 'binary',
  shuffle = False,
  seed = 42
)
```

```
## Found 40 images belonging to 2 classes.
```

```python
test_generator = test_datagen.flow_from_directory(
  'dogs/test/',
  target_size = (224,224),
  color_mode = 'rgb',
  batch_size = 10,
  class_mode = 'binary',
  shuffle = False,
  seed = 42
)
```

```
## Found 40 images belonging to 2 classes.
```

---

```python
model = Sequential()
model.add(Conv2D(filters=32, input_shape=((224, 224, 3)),
  kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
  optimizer='adam', metrics=['accuracy'])

step_size_train = 50
step_size_valid = valid_generator.n // valid_generator.batch_size
step_size_test = test_generator.n // test_generator.batch_size

callbacks = [EarlyStopping(monitor='val_loss', patience=5,
  restore_best_weights=True)]

history = model.fit(train_generator, steps_per_epoch = step_size_train,
  epochs = 50, callbacks = callbacks, validation_data = valid_generator,
  validation_steps = step_size_valid, verbose = 0)
```

---

```python
y_true = test_generator.classes
y_pred = model.predict(test_generator, steps = step_size_test)
y_pred = (y_pred > 0.5).astype(int).reshape(y_true.shape)

print(np.mean(y_true == y_pred))
```

```
## 0.65
```

```python
pd.DataFrame(
  confusion_matrix(y_true, y_pred), 
  index=['true:yes', 'true:no'], 
  columns=['pred:yes', 'pred:no']
)
```

```
##           pred:yes  pred:no
## true:yes        14        6
## true:no          8       12
```

---

For my next trick I shall need someone else's hard work.

```python
from tensorflow.keras.applications.mobilenet import MobileNet, preprocess_input
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras import Model

base_model = MobileNet(weights='imagenet', include_top=False)
```

```
## --- Logging error ---
## Traceback (most recent call last):
##   File "C:\Users\gsimc\AppData\Local\Programs\Python\Python38\lib\logging\__init__.py", line 1084, in emit
##     stream.write(msg + self.terminator)
## ValueError: I/O operation on closed file
## Call stack:
##   File "<string>", line 1, in <module>
##   File "C:\Users\gsimc\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\applications\mobilenet.py", line 216, in MobileNet
##     logging.warning('`input_shape` is undefined or non-square, '
##   File "C:\Users\gsimc\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\platform\tf_logging.py", line 178, in warning
##     get_logger().warning(msg, *args, **kwargs)
## Message: '`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.'
## Arguments: ()
## --- Logging error ---
## Traceback (most recent call last):
##   File "C:\Users\gsimc\AppData\Local\Programs\Python\Python38\lib\logging\__init__.py", line 1084, in emit
##     stream.write(msg + self.terminator)
## ValueError: I/O operation on closed file
## Call stack:
##   File "<string>", line 1, in <module>
##   File "C:\Users\gsimc\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\applications\mobilenet.py", line 216, in MobileNet
##     logging.warning('`input_shape` is undefined or non-square, '
##   File "C:\Users\gsimc\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\platform\tf_logging.py", line 178, in warning
##     get_logger().warning(msg, *args, **kwargs)
## Message: '`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.'
## Arguments: ()
```

---

```python
l = base_model.output
l = GlobalAveragePooling2D()(l)
out = Dense(1, activation='sigmoid')(l)

model = Model(inputs = base_model.input, outputs = out)

for layer in model.layers[:20]:
    layer.trainable = False
for layer in model.layers[20:]:
    layer.trainable = True

model.compile(loss='binary_crossentropy',
  optimizer='adam', metrics=['accuracy'])
```

---

```python
train_datagen = ImageDataGenerator(
  preprocessing_function = preprocess_input,
  rotation_range = 30,
	zoom_range = 0.15,
	width_shift_range = 0.2,
	height_shift_range = 0.2,
	shear_range = 0.15,
	horizontal_flip = True,
	fill_mode = 'nearest'
)

valid_datagen = ImageDataGenerator(
  preprocessing_function = preprocess_input
)

test_datagen = ImageDataGenerator(
  preprocessing_function = preprocess_input
)
```

---

```
## Found 120 images belonging to 2 classes.
```

```
## Found 40 images belonging to 2 classes.
```

---

```
## Found 40 images belonging to 2 classes.
```

```python
step_size_train = 50
step_size_valid = valid_generator.n // valid_generator.batch_size
step_size_test = test_generator.n // test_generator.batch_size

callbacks = [EarlyStopping(monitor='val_loss', patience=5,
  restore_best_weights=True)]

history = model.fit(train_generator, steps_per_epoch = step_size_train,
  epochs = 50, callbacks = callbacks, validation_data = valid_generator,
  validation_steps = step_size_valid, verbose = 0)
```

---

```python
y_true = test_generator.classes
y_pred = model.predict(test_generator, steps = step_size_test)
y_pred = (y_pred > 0.5).astype(int).reshape(y_true.shape)

print(np.mean(y_true == y_pred))
```

```
## 0.825
```

```python
pd.DataFrame(
  confusion_matrix(y_true, y_pred), 
  index=['true:yes', 'true:no'], 
  columns=['pred:yes', 'pred:no']
)
```

```
##           pred:yes  pred:no
## true:yes        19        1
## true:no          6       14
```

---

### What just happened?

- The ImageNet networks have learned to differentiate between 120 dog breeds!
- Clearly there's some knowledge about dogs gained in the low-level layers (features)
- Freezing those layers and harnessing that output for our purpose makes sense
- Think of it as a "warm start"
- After a few epochs try to unfreeze the low layers then use a very small learning rate for a few additional epochs, to gain some more predictive power

---

# Other Computer Vision Tasks

---

### What's Next?

- Localization
- Object Detection
- Segmentation
- Captioning
- 3D Reconstruction
- Restoration
- Pose Estimation
- GANs GANs GANs
- Doing it all on Video
- And doing it all in real time, on your phone or in your car