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!wget --no-check-certificate \
https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \
-O /tmp/horse-or-human.zip
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!wget --no-check-certificate \
https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip \
-O /tmp/validation-horse-or-human.zip
The following python code will use the OS library to use Operating System libraries, giving you access to the file system, and the zipfile library allowing you to unzip the data.
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import os
import zipfile
local_zip = '/tmp/horse-or-human.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp/horse-or-human')
local_zip = '/tmp/validation-horse-or-human.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp/validation-horse-or-human')
zip_ref.close()
The contents of the .zip are extracted to the base directory /tmp/horse-or-human, which in turn each contain horses and humans subdirectories.
In short: The training set is the data that is used to tell the neural network model that 'this is what a horse looks like', 'this is what a human looks like' etc.
One thing to pay attention to in this sample: We do not explicitly label the images as horses or humans. If you remember with the handwriting example earlier, we had labelled 'this is a 1', 'this is a 7' etc. Later you'll see something called an ImageGenerator being used -- and this is coded to read images from subdirectories, and automatically label them from the name of that subdirectory. So, for example, you will have a 'training' directory containing a 'horses' directory and a 'humans' one. ImageGenerator will label the images appropriately for you, reducing a coding step.
Let's define each of these directories:
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# Directory with our training horse pictures
train_horse_dir = os.path.join('/tmp/horse-or-human/horses')
# Directory with our training human pictures
train_human_dir = os.path.join('/tmp/horse-or-human/humans')
# Directory with our training horse pictures
validation_horse_dir = os.path.join('/tmp/validation-horse-or-human/horses')
# Directory with our training human pictures
validation_human_dir = os.path.join('/tmp/validation-horse-or-human/humans')
Now, let's see what the filenames look like in the horses and humans training directories:
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train_horse_names = os.listdir(train_horse_dir)
print(train_horse_names[:10])
train_human_names = os.listdir(train_human_dir)
print(train_human_names[:10])
validation_horse_hames = os.listdir(validation_horse_dir)
print(validation_horse_hames[:10])
validation_human_names = os.listdir(validation_human_dir)
print(validation_human_names[:10])
Let's find out the total number of horse and human images in the directories:
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print('total training horse images:', len(os.listdir(train_horse_dir)))
print('total training human images:', len(os.listdir(train_human_dir)))
print('total validation horse images:', len(os.listdir(validation_horse_dir)))
print('total validation human images:', len(os.listdir(validation_human_dir)))
Now let's take a look at a few pictures to get a better sense of what they look like. First, configure the matplot parameters:
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%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Parameters for our graph; we'll output images in a 4x4 configuration
nrows = 4
ncols = 4
# Index for iterating over images
pic_index = 0
Now, display a batch of 8 horse and 8 human pictures. You can rerun the cell to see a fresh batch each time:
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# Set up matplotlib fig, and size it to fit 4x4 pics
fig = plt.gcf()
fig.set_size_inches(ncols * 4, nrows * 4)
pic_index += 8
next_horse_pix = [os.path.join(train_horse_dir, fname)
for fname in train_horse_names[pic_index-8:pic_index]]
next_human_pix = [os.path.join(train_human_dir, fname)
for fname in train_human_names[pic_index-8:pic_index]]
for i, img_path in enumerate(next_horse_pix+next_human_pix):
# Set up subplot; subplot indices start at 1
sp = plt.subplot(nrows, ncols, i + 1)
sp.axis('Off') # Don't show axes (or gridlines)
img = mpimg.imread(img_path)
plt.imshow(img)
plt.show()
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import tensorflow as tf
We then add convolutional layers as in the previous example, and flatten the final result to feed into the densely connected layers.
Finally we add the densely connected layers.
Note that because we are facing a two-class classification problem, i.e. a binary classification problem, we will end our network with a sigmoid activation, so that the output of our network will be a single scalar between 0 and 1, encoding the probability that the current image is class 1 (as opposed to class 0).
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model = tf.keras.models.Sequential([
# Note the input shape is the desired size of the image 300x300 with 3 bytes color
# This is the first convolution
tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(300, 300, 3)),
tf.keras.layers.MaxPooling2D(2, 2),
# The second convolution
tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# The third convolution
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# The fourth convolution
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# The fifth convolution
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# Flatten the results to feed into a DNN
tf.keras.layers.Flatten(),
# 512 neuron hidden layer
tf.keras.layers.Dense(512, activation='relu'),
# Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('horses') and 1 for the other ('humans')
tf.keras.layers.Dense(1, activation='sigmoid')
])
The model.summary() method call prints a summary of the NN
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model.summary()
The "output shape" column shows how the size of your feature map evolves in each successive layer. The convolution layers reduce the size of the feature maps by a bit due to padding, and each pooling layer halves the dimensions.
Next, we'll configure the specifications for model training. We will train our model with the binary_crossentropy loss, because it's a binary classification problem and our final activation is a sigmoid. (For a refresher on loss metrics, see the Machine Learning Crash Course.) We will use the rmsprop optimizer with a learning rate of 0.001. During training, we will want to monitor classification accuracy.
NOTE: In this case, using the RMSprop optimization algorithm is preferable to stochastic gradient descent (SGD), because RMSprop automates learning-rate tuning for us. (Other optimizers, such as Adam and Adagrad, also automatically adapt the learning rate during training, and would work equally well here.)
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from tensorflow.keras.optimizers import RMSprop
model.compile(loss='binary_crossentropy',
optimizer=RMSprop(lr=0.001),
metrics=['acc'])
Let's set up data generators that will read pictures in our source folders, convert them to float32 tensors, and feed them (with their labels) to our network. We'll have one generator for the training images and one for the validation images. Our generators will yield batches of images of size 300x300 and their labels (binary).
As you may already know, data that goes into neural networks should usually be normalized in some way to make it more amenable to processing by the network. (It is uncommon to feed raw pixels into a convnet.) In our case, we will preprocess our images by normalizing the pixel values to be in the [0, 1] range (originally all values are in the [0, 255] range).
In Keras this can be done via the keras.preprocessing.image.ImageDataGenerator class using the rescale parameter. This ImageDataGenerator class allows you to instantiate generators of augmented image batches (and their labels) via .flow(data, labels) or .flow_from_directory(directory). These generators can then be used with the Keras model methods that accept data generators as inputs: fit_generator, evaluate_generator, and predict_generator.
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale=1/255)
validation_datagen = ImageDataGenerator(rescale=1/255)
# Flow training images in batches of 128 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
'/tmp/horse-or-human/', # This is the source directory for training images
target_size=(300, 300), # All images will be resized to 150x150
batch_size=128,
# Since we use binary_crossentropy loss, we need binary labels
class_mode='binary')
# Flow training images in batches of 128 using train_datagen generator
validation_generator = validation_datagen.flow_from_directory(
'/tmp/validation-horse-or-human/', # This is the source directory for training images
target_size=(300, 300), # All images will be resized to 150x150
batch_size=32,
# Since we use binary_crossentropy loss, we need binary labels
class_mode='binary')
Let's train for 15 epochs -- this may take a few minutes to run.
Do note the values per epoch.
The Loss and Accuracy are a great indication of progress of training. It's making a guess as to the classification of the training data, and then measuring it against the known label, calculating the result. Accuracy is the portion of correct guesses.
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history = model.fit_generator(
train_generator,
steps_per_epoch=8,
epochs=15,
verbose=1,
validation_data = validation_generator,
validation_steps=8)
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import numpy as np
from google.colab import files
from keras.preprocessing import image
uploaded = files.upload()
for fn in uploaded.keys():
# predicting images
path = '/content/' + fn
img = image.load_img(path, target_size=(300, 300))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict(images, batch_size=10)
print(classes[0])
if classes[0]>0.5:
print(fn + " is a human")
else:
print(fn + " is a horse")
To get a feel for what kind of features our convnet has learned, one fun thing to do is to visualize how an input gets transformed as it goes through the convnet.
Let's pick a random image from the training set, and then generate a figure where each row is the output of a layer, and each image in the row is a specific filter in that output feature map. Rerun this cell to generate intermediate representations for a variety of training images.
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import numpy as np
import random
from tensorflow.keras.preprocessing.image import img_to_array, load_img
# Let's define a new Model that will take an image as input, and will output
# intermediate representations for all layers in the previous model after
# the first.
successive_outputs = [layer.output for layer in model.layers[1:]]
#visualization_model = Model(img_input, successive_outputs)
visualization_model = tf.keras.models.Model(inputs = model.input, outputs = successive_outputs)
# Let's prepare a random input image from the training set.
horse_img_files = [os.path.join(train_horse_dir, f) for f in train_horse_names]
human_img_files = [os.path.join(train_human_dir, f) for f in train_human_names]
img_path = random.choice(horse_img_files + human_img_files)
img = load_img(img_path, target_size=(300, 300)) # this is a PIL image
x = img_to_array(img) # Numpy array with shape (150, 150, 3)
x = x.reshape((1,) + x.shape) # Numpy array with shape (1, 150, 150, 3)
# Rescale by 1/255
x /= 255
# Let's run our image through our network, thus obtaining all
# intermediate representations for this image.
successive_feature_maps = visualization_model.predict(x)
# These are the names of the layers, so can have them as part of our plot
layer_names = [layer.name for layer in model.layers]
# Now let's display our representations
for layer_name, feature_map in zip(layer_names, successive_feature_maps):
if len(feature_map.shape) == 4:
# Just do this for the conv / maxpool layers, not the fully-connected layers
n_features = feature_map.shape[-1] # number of features in feature map
# The feature map has shape (1, size, size, n_features)
size = feature_map.shape[1]
# We will tile our images in this matrix
display_grid = np.zeros((size, size * n_features))
for i in range(n_features):
# Postprocess the feature to make it visually palatable
x = feature_map[0, :, :, i]
x -= x.mean()
x /= x.std()
x *= 64
x += 128
x = np.clip(x, 0, 255).astype('uint8')
# We'll tile each filter into this big horizontal grid
display_grid[:, i * size : (i + 1) * size] = x
# Display the grid
scale = 20. / n_features
plt.figure(figsize=(scale * n_features, scale))
plt.title(layer_name)
plt.grid(False)
plt.imshow(display_grid, aspect='auto', cmap='viridis')
As you can see we go from the raw pixels of the images to increasingly abstract and compact representations. The representations downstream start highlighting what the network pays attention to, and they show fewer and fewer features being "activated"; most are set to zero. This is called "sparsity." Representation sparsity is a key feature of deep learning.
These representations carry increasingly less information about the original pixels of the image, but increasingly refined information about the class of the image. You can think of a convnet (or a deep network in general) as an information distillation pipeline.
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import os, signal
os.kill(os.getpid(), signal.SIGKILL)