In [1]:

    

from __future__ import print_function
import tensorflow as tf
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dropout, Activation, Convolution2D, GlobalAveragePooling2D, merge
from keras.utils import np_utils
from keras.optimizers import SGD
from keras import backend as K
from keras.models import Model
from keras.layers.core import Lambda
from keras.callbacks import ModelCheckpoint
import pandas
import cv2
import numpy as np
import matplotlib.pyplot as plt


    

    




Using TensorFlow backend.

In [2]:

    

K.set_image_dim_ordering('tf')

batch_size = 32
nb_classes = 10
nb_epoch = 350

rows, cols = 32, 32

channels = 3


    

In [3]:

    

(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')

print (X_train.shape[1:])

Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)


    

    




X_train shape: (50000, 32, 32, 3)
50000 train samples
10000 test samples
(32, 32, 3)

In [4]:

    

imgid = 0
image = X_train[imgid]
#image = image.transpose([1, 2, 0])
image = image.astype('float32')
image /= 255

%matplotlib inline
imgplot = plt.imshow(image)

categoryid = y_train[imgid]


    

In [5]:

    

model = Sequential()

model.add(Convolution2D(96, 3, 3, border_mode = 'same', input_shape=(32, 32, 3)))
model.add(Activation('relu'))
model.add(Convolution2D(96, 3, 3,border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(96, 3, 3, border_mode='same', subsample = (2,2)))
model.add(Dropout(0.5))

model.add(Convolution2D(192, 3, 3, border_mode = 'same'))
model.add(Activation('relu'))
model.add(Convolution2D(192, 3, 3,border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(192, 3, 3,border_mode='same', subsample = (2,2)))
model.add(Dropout(0.5))

model.add(Convolution2D(192, 3, 3, border_mode = 'same'))
model.add(Activation('relu'))
model.add(Convolution2D(192, 1, 1,border_mode='valid'))
model.add(Activation('relu'))
model.add(Convolution2D(10, 1, 1, border_mode='valid'))

model.add(GlobalAveragePooling2D())
model.add(Activation('softmax'))
#model = make_parallel(model, 4)
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)


    

    




/usr/lib/python2.7/site-packages/ipykernel_launcher.py:3: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(96, (3, 3), padding="same", input_shape=(32, 32, 3...)`
  This is separate from the ipykernel package so we can avoid doing imports until
/usr/lib/python2.7/site-packages/ipykernel_launcher.py:5: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(96, (3, 3), padding="same")`
  """
/usr/lib/python2.7/site-packages/ipykernel_launcher.py:7: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(96, (3, 3), padding="same", strides=(2, 2))`
  import sys
/usr/lib/python2.7/site-packages/ipykernel_launcher.py:10: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(192, (3, 3), padding="same")`
  # Remove the CWD from sys.path while we load stuff.
/usr/lib/python2.7/site-packages/ipykernel_launcher.py:12: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(192, (3, 3), padding="same")`
  if sys.path[0] == '':
/usr/lib/python2.7/site-packages/ipykernel_launcher.py:14: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(192, (3, 3), padding="same", strides=(2, 2))`
  
/usr/lib/python2.7/site-packages/ipykernel_launcher.py:17: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(192, (3, 3), padding="same")`
/usr/lib/python2.7/site-packages/ipykernel_launcher.py:19: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(192, (1, 1), padding="valid")`
/usr/lib/python2.7/site-packages/ipykernel_launcher.py:21: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(10, (1, 1), padding="valid")`

In [6]:

    

model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
print (model.summary())


    

    




_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 32, 32, 96)        2688      
_________________________________________________________________
activation_1 (Activation)    (None, 32, 32, 96)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 32, 32, 96)        83040     
_________________________________________________________________
activation_2 (Activation)    (None, 32, 32, 96)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 16, 16, 96)        83040     
_________________________________________________________________
dropout_1 (Dropout)          (None, 16, 16, 96)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 16, 16, 192)       166080    
_________________________________________________________________
activation_3 (Activation)    (None, 16, 16, 192)       0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 16, 16, 192)       331968    
_________________________________________________________________
activation_4 (Activation)    (None, 16, 16, 192)       0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 8, 8, 192)         331968    
_________________________________________________________________
dropout_2 (Dropout)          (None, 8, 8, 192)         0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 8, 8, 192)         331968    
_________________________________________________________________
activation_5 (Activation)    (None, 8, 8, 192)         0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 8, 8, 192)         37056     
_________________________________________________________________
activation_6 (Activation)    (None, 8, 8, 192)         0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 8, 8, 10)          1930      
_________________________________________________________________
global_average_pooling2d_1 ( (None, 10)                0         
_________________________________________________________________
activation_7 (Activation)    (None, 10)                0         
=================================================================
Total params: 1,369,738
Trainable params: 1,369,738
Non-trainable params: 0
_________________________________________________________________
None

In [7]:

    

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

print(X_train.shape[0])

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) 

datagen.fit(X_train)
print(X_train.shape[0])

filepath="weights.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='max')

callbacks_list = [checkpoint]

# Fit the model on the batches generated by datagen.flow().
history_callback = model.fit_generator(datagen.flow(X_train, Y_train,
                                    batch_size=batch_size),
                                    samples_per_epoch=X_train.shape[0],
                                    nb_epoch=nb_epoch,
                                    validation_data=(X_test, Y_test),
                                    callbacks=callbacks_list, verbose=1)

im = cv2.resize(cv2.imread('image.jpg'), (224, 224)).astype(np.float32)
out = model.predict(im)
print (np.argmax(out))

pandas.DataFrame(history_callback.history).to_csv("history.csv")

model.save('keras_allconv.h5')


    

    




50000
50000






    




/usr/lib/python2.7/site-packages/ipykernel_launcher.py:34: UserWarning: The semantics of the Keras 2 argument `steps_per_epoch` is not the same as the Keras 1 argument `samples_per_epoch`. `steps_per_epoch` is the number of batches to draw from the generator at each epoch. Basically steps_per_epoch = samples_per_epoch/batch_size. Similarly `nb_val_samples`->`validation_steps` and `val_samples`->`steps` arguments have changed. Update your method calls accordingly.
/usr/lib/python2.7/site-packages/ipykernel_launcher.py:34: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<keras_pre..., verbose=1, validation_data=(array([[[..., steps_per_epoch=1562, epochs=350, callbacks=[<keras.ca...)`






    




Epoch 1/350
1562/1562 [==============================] - 56s 36ms/step - loss: 1.9416 - acc: 0.2566 - val_loss: 1.6051 - val_acc: 0.3933

Epoch 00001: val_acc improved from -inf to 0.39330, saving model to weights.hdf5
Epoch 2/350
1562/1562 [==============================] - 54s 34ms/step - loss: 1.5343 - acc: 0.4280 - val_loss: 1.3193 - val_acc: 0.5050

Epoch 00002: val_acc improved from 0.39330 to 0.50500, saving model to weights.hdf5
Epoch 3/350
 825/1562 [==============>...............] - ETA: 23s - loss: 1.3257 - acc: 0.5188





    





KeyboardInterruptTraceback (most recent call last)
<ipython-input-7-376612ee6a5e> in <module>()
     32                                     nb_epoch=nb_epoch,
     33                                     validation_data=(X_test, Y_test),
---> 34                                     callbacks=callbacks_list, verbose=1)
     35 
     36 im = cv2.resize(cv2.imread('image.jpg'), (224, 224)).astype(np.float32)

/usr/lib64/python2.7/site-packages/keras/legacy/interfaces.pyc in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/lib64/python2.7/site-packages/keras/engine/training.pyc in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1416             use_multiprocessing=use_multiprocessing,
   1417             shuffle=shuffle,
-> 1418             initial_epoch=initial_epoch)
   1419 
   1420     @interfaces.legacy_generator_methods_support

/usr/lib64/python2.7/site-packages/keras/engine/training_generator.pyc in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    215                 outs = model.train_on_batch(x, y,
    216                                             sample_weight=sample_weight,
--> 217                                             class_weight=class_weight)
    218 
    219                 outs = to_list(outs)

/usr/lib64/python2.7/site-packages/keras/engine/training.pyc in train_on_batch(self, x, y, sample_weight, class_weight)
   1215             ins = x + y + sample_weights
   1216         self._make_train_function()
-> 1217         outputs = self.train_function(ins)
   1218         return unpack_singleton(outputs)
   1219 

/usr/lib64/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in __call__(self, inputs)
   2713                 return self._legacy_call(inputs)
   2714 
-> 2715             return self._call(inputs)
   2716         else:
   2717             if py_any(is_tensor(x) for x in inputs):

/usr/lib64/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in _call(self, inputs)
   2673             fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
   2674         else:
-> 2675             fetched = self._callable_fn(*array_vals)
   2676         return fetched[:len(self.outputs)]
   2677 

/usr/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in __call__(self, *args, **kwargs)
   1397           ret = tf_session.TF_SessionRunCallable(
   1398               self._session._session, self._handle, args, status,
-> 1399               run_metadata_ptr)
   1400         if run_metadata:
   1401           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

KeyboardInterrupt: 

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