Image Classification with Keras

The Keras project is a high-level Python neural network API. It's designed to be both user friendly and modular, supporting multiple backends. The default backend is Tensorflow. We'll use Tensorflow to classify the CIFAR-10 image dataset.

1.
Setup

To run Keras with Tensorflow, we'll need tensorflow-gpu, the CUDA neural networks libraries, and the Python HDF5 package. We'll also add a couple of graphical packages that Keras can use to visualize models.

conda install -qy -c anaconda tensorflow-gpu h5py graphviz pydot
pip install keras dill















































































The CIFAR-10 dataset is a collection of 60,000 color, 32x32-pixel images in ten classes, 10,000 of which are in a test batch. Keras can automatically download the dataset, but we'll save time by downloading in the Appendix, and just copying that file to the right place and unzipping it. We'll lock this cell and the setup cell above after the first run so that we shouldn't need to redownload.

mkdir -p ~/.keras/datasets
cd ~/.keras/datasets
cp fetch cifar data.cifar-10-python.tar.gz ./cifar-10-batches-py.tar.gz
tar -zxf cifar-10-batches-py.tar.gz

We'll run 128-image batches and set up two training runs: a long, 500-epoch run to do the main work, and a short, 5-epoch run as an example.

batch_size = 128
num_classes = 10
epochs_shortrun = 5
epochs_longrun = 500
save_dir = "/work"
res_dir = "/results"
model_name = 'convnet_cifar10'

Load the data and get it into a reasonable shape. Also set up a function to find the best checkpoint file, another to give us a look at the images we're analyzing, and finally set up to do real-time input-data augmentation.

1.6s
from __future__ import print_function
import tensorflow as tf
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D
from keras.optimizers import Adam
from keras.layers.pooling import MaxPooling2D
from keras.callbacks import ModelCheckpoint,EarlyStopping
from keras.utils import to_categorical
from keras.models import load_model

import os
import dill as pickle
import numpy as np

# set random seeds for reproducibility
tf.reset_default_graph()
tf.set_random_seed(343)
np.random.seed(343)

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

# The data, shuffled and split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# Convert class vectors to binary class matrices.
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255.0
x_test /= 255.0

# Load label names to use in prediction results
label_list_path = 'datasets/cifar-10-batches-py/batches.meta'

keras_dir = os.path.expanduser(os.path.join('~', '.keras'))
datadir_base = os.path.expanduser(keras_dir)
if not os.access(datadir_base, os.W_OK):
    datadir_base = os.path.join('/tmp', '.keras')
label_list_path = os.path.join(datadir_base, label_list_path)

with open(label_list_path, mode='rb') as f:
    labels = pickle.load(f)

ckpt_dir = os.path.join(save_dir,"checkpoints")
if not os.path.isdir(ckpt_dir):
    os.makedirs(ckpt_dir)

model_path = os.path.join(res_dir, model_name + ".kerasave")
hist_path = os.path.join(res_dir, model_name + ".kerashist")

# Function to find latest checkpoint file
def last_ckpt(dir):
  fl = os.listdir(dir)
  fl = [x for x in fl if x.endswith(".hdf5")]
  cf = ""
  if len(fl) > 0:
    accs = [float(x.split("-")[3][0:-5]) for x in fl]
    m = max(accs)
    iaccs = [i for i, j in enumerate(accs) if j == m]
    fl = [fl[x] for x in iaccs]
    epochs = [int(x.split("-")[2]) for x in fl]
    cf = fl[epochs.index(max(epochs))]
    cf = os.path.join(dir,cf)
  
  return(cf)

import matplotlib.pyplot as plt
from math import *

#Visualizing CIFAR 10, takes indicides and shows in a grid
def cifar_grid(X,Y,inds,n_col, predictions=None):
  if predictions is not None:
    if Y.shape != predictions.shape:
      print("Predictions must equal Y in length!")
      return(None)
  N = len(inds)
  n_row = int(ceil(1.0*N/n_col))
  fig, axes = plt.subplots(n_row,n_col,figsize=(10,10))
  
  clabels = labels["label_names"]
  for j in range(n_row):
    for k in range(n_col):
      i_inds = j*n_col+k
      i_data = inds[i_inds]
      
      axes[j][k].set_axis_off()
      if i_inds < N:
        axes[j][k].imshow(X[i_data,...], interpolation='nearest')
        label = clabels[np.argmax(Y[i_data,...])]
        axes[j][k].set_title(label)
        if predictions is not None:
          pred = clabels[np.argmax(predictions[i_data,...])]
          if label != pred:
            label += " n"
            axes[j][k].set_title(pred, color='red')            
  
  fig.set_tight_layout(True)
  return(fig)

print('Using real-time data augmentation.')
# This will do preprocessing and realtime data augmentation:
datagen = ImageDataGenerator(
	featurewise_center=False,  # set input mean to 0 over the dataset
	samplewise_center=False,  # set each sample mean to 0
	featurewise_std_normalization=False,  # divide inputs by std of the dataset
	samplewise_std_normalization=False,  # divide each input by its std
	zca_whitening=False,  # apply ZCA whitening
	rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
	width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
	height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
	horizontal_flip=True,  # randomly flip images
	vertical_flip=False)  # randomly flip images

# Compute quantities required for feature-wise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(x_train)






Let's take a gander at a random selection of training images.

indices = [np.random.choice(range(len(x_train))) for i in range(36)]

cifar_grid(x_train,y_train,indices,6)

We'll use a simple convolutional network model (still under development), with the addition of the data augmentation defined above, and a checkpoint-writing callback that's keyed to significant accuracy improvements.

0.4s
model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',
                 input_shape=x_train.shape[1:]))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))


# initiate Adam optimizer
opt = Adam(lr=0.0001, decay=1e-6)

# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
              optimizer=opt,
              metrics=['accuracy'])

# checkpoint callback
filepath = os.path.join(ckpt_dir,
    "weights-improvement-{epoch:02d}-{val_acc:.6f}.hdf5")
checkpoint = ModelCheckpoint(
  filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max'
)
print("Saving improvement checkpoints to \n\t{0}".format(filepath))

# early stop callback, given a bit more leeway
stahp = EarlyStopping(min_delta=0.00001, patience=25)



Finally, let's take a look at our model, with both a text summary and a flow chart.

from keras.utils import plot_model
plot_model(model, to_file="/results/model.svg", 
           show_layer_names=True, show_shapes=True, rankdir="TB")
print(model.summary())



































2.
Training

Now we're ready to train using the GPU. We'll set up two code branches: one to do some serious long-term training (takes hours), and one which just runs a few additional epochs as an example. Note that both act as endpoints to their inheritance branches, because the underlying system that allows inheritance does not currenly know how to handle GPU activity. So, the training cells will save their results to /files/, and then for analysis and visualization we'll just need to load that data. We'll also pickle the training history for the long run to a file, in case we want to take a look at that.

Both paths are able to load the most accurate of any existing checkpoints in /files/checkpoints—this would allow for additional refinement of accuracy, although such runs would probably require removal or relaxation of the EarlyStopping callback.

2.1.
Long Training

epochs = settings.epochs_longrun

cpf = last_ckpt(ckpt_dir)
if cpf != "":
  print("Loading starting weights from \n\t{0}".format(cpf))
  model.load_weights(cpf)

# Fit the model on the batches generated by datagen.flow().
hist = model.fit_generator(datagen.flow(x_train, y_train,
    batch_size=batch_size,shuffle=True),
    steps_per_epoch=x_train.shape[0] // batch_size,
    epochs=epochs,verbose=2,
    validation_data=(x_test, y_test),
    workers=4, callbacks=[checkpoint,stahp])

# Save model and weights
model.save(model_path)
#print('Saved trained model at %s ' % model_path)

with open(hist_path, 'wb') as f:
  pickle.dump(hist.history, f)









































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































convnet_cifar10.kerashist
Download
convnet_cifar10.kerasave
Download

2.2.
Short Example

epochs = settings.epochs_shortrun

# load results of long training run
model.load_weights(long training.convnet_cifar10.kerasave)

# Fit the model on the batches generated by datagen.flow().
hist = model.fit_generator(datagen.flow(x_train, y_train,
    batch_size=batch_size),
    steps_per_epoch=x_train.shape[0] // batch_size,
    epochs=epochs,verbose=2,
    validation_data=(x_test, y_test),
    workers=4, callbacks=[checkpoint])

# Save model and weights
model.save(model_path)
print('Saved trained model at %s ' % model_path)






















convnet_cifar10.kerasave
Download

3.
Results

Alrighty, now we can take a look at the trained model. The load_model() function will give us back our full, trained model for evaluation and prediction.

from keras.models import load_model

import os
import dill as pickle
import numpy as np

model = load_model(short training.convnet_cifar10.kerasave)

# Evaluate model with test data set
evaluation = model.evaluate_generator(datagen.flow(x_test, y_test,
    batch_size=batch_size, shuffle=False),
    steps=x_test.shape[0] // batch_size, workers=4)

# Print out final values of all metrics
key2name = {'acc':'Accuracy', 'loss':'Loss', 
    'val_acc':'Validation Accuracy', 'val_loss':'Validation Loss'}
results = []
for i,key in enumerate(model.metrics_names):
    results.append('%s = %.2f' % (key2name[key], evaluation[i]))
print(", ".join(results))


We can sample the prediction results with images.

num_predictions = 36

model = load_model(short training.convnet_cifar10.kerasave)

predict_gen = model.predict_generator(datagen.flow(x_test, y_test,
    batch_size=batch_size, shuffle=False),
    steps=(x_test.shape[0] // batch_size)+1, workers=4)

indices = [np.random.choice(range(len(x_test))) 
           for i in range(num_predictions)]

cifar_grid(x_test,y_test,indices,6, predictions=predict_gen)

And hey, let's take a look at the training history (we'll look at the long training so it's an interesting history).

import matplotlib
import matplotlib.pyplot as plt

with open(long training.convnet_cifar10.kerashist, 'rb') as f:
  hist = pickle.load(f)

key2name = {'acc':'Accuracy', 'loss':'Loss', 
    'val_acc':'Validation Accuracy', 'val_loss':'Validation Loss'}

fig = plt.figure()

things = ['acc','loss','val_acc','val_loss']
for i,thing in enumerate(things):
  trace = hist[thing]
  plt.subplot(2,2,i+1)
  plt.plot(range(len(trace)),trace)
  plt.title(key2name[thing])

fig.set_tight_layout(True)
fig

Finally, let's see if our trained network can correctly identify the subject of an uploaded image. This is the Internet, so it must be a cat.

from keras import backend as K
sess = K.get_session()

model = load_model(long training.convnet_cifar10.kerasave)

img = tf.read_file(Qat.jpg)
img = tf.image.decode_jpeg(img, channels=3)
img.set_shape([None, None, 3])
img = tf.image.resize_images(img, (32, 32))
img = img.eval(session=sess) # convert to numpy array
img = np.expand_dims(img, 0) # make 'batch' of 1

pred = model.predict(img)
pred = labels["label_names"][np.argmax(pred)]


Magic 8-ball says this image contains a cat. Huzzah!