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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.


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 -y -c anaconda \
  tensorflow-gpu h5py graphviz pydot \
  cudatoolkit=8 # compat with installed driver
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 cifar-10-python.tar.gzfetch cifar data ./cifar-10-batches-py.tar.gz
tar -zxf cifar-10-batches-py.tar.gz

A lot of initilization code will be needed multiple times, so we'll put it in its own runtime and then import the desired code cells where we need it.

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"

# setup paths
import os

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

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

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.

module and data functions
Setup (Python)Installs
import numpy as np
import dill as pickle
from math import *

def setup_tf():
  # set random seeds for reproducibility

def setup_load_cifar(verbose=False):
  from keras.datasets import cifar10
  from keras.utils import to_categorical
  # The data, shuffled and split between train and test sets:
  (x_train, y_train), (x_test, y_test) = cifar10.load_data()
  if verbose:
    print("x_train shape: {}, {} train samples, {} test samples.\n".format(
      x_train.shape, x_train.shape[0], x_test.shape[0]))
  # 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)
  return x_train, y_train, x_test, y_test, labels

def setup_data_aug():
  print("Using real-time data augmentation.\n")
  # This will do preprocessing and realtime data augmentation:
  from keras.preprocessing.image import ImageDataGenerator
  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
  return datagen

# 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

#Visualizing CIFAR 10, takes indicides and shows in a grid
def cifar_grid(X,Y,inds,n_col, predictions=None):
  import matplotlib.pyplot as plt
  if predictions is not None:
    if Y.shape != predictions.shape:
      print("Predictions must equal Y in length!\n")
  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]
      if i_inds < N:
        axes[j][k].imshow(X[i_data,...], interpolation="nearest")
        label = clabels[np.argmax(Y[i_data,...])]
        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")
  return fig

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

x_train, y_train, x_test, y_test, labels = setup_load_cifar(verbose=True)

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


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.

define model
Setup (Python)Installs
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

model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3), activation="relu",
model.add(Conv2D(64, 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(Conv2D(128, kernel_size=(3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dense(1024, activation="relu"))
model.add(Dense(10, activation="softmax"))

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

# Let's train the model using RMSprop
              optimizer=opt, metrics=["accuracy"])

# checkpoint callback
filepath = os.path.join(ckpt_dir,
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")


Now we're ready to train using the GPU. We'll put this in a separate runtime configured to use a dedicated GPU compute node. This will have an initialization cell, and then two training cells: one to do some serious long-term training (takes hours), and one which just runs a few additional epochs as an example. 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.

from __future__ import print_function

#os.environ["CUDA_VISIBLE_DEVICES"] = "" # for testing
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

x_train, y_train, x_test, y_test, labels = setup_load_cifar(verbose=True)

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

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.

Long Training

epochs = epochs_longrun

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

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

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

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


Short Example

epochs = epochs_shortrun

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

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

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



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 __future__ import print_function

from keras.models import load_model

x_train, y_train, x_test, y_test, labels = setup_load_cifar()
datagen = setup_data_aug()

model = load_model(convnet_cifar10.kerasaveshort training)

# 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(convnet_cifar10.kerasaveshort training)

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.pyplot as plt

with open(convnet_cifar10.kerashistlong training, '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]


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
from keras.models import load_model
import tensorflow as tf

sess = K.get_session()

model = load_model(convnet_cifar10.kerasavelong training)
_,_,_,_,labels = setup_load_cifar()

img = tf.read_file(Image)
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 nilcustom analysis. Huzzah!