In [1]:

    

from keras.applications import VGG16
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras import models
from keras import layers
from keras import optimizers
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from livelossplot import PlotLossesKeras
from keras.optimizers import Adam
from keras.models import Sequential, Model, load_model
from keras.layers import Dropout, Flatten, Dense


    

    




/home/dex/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.

In [2]:

    

(x_train, y_train), (x_test, y_test) = cifar10.load_data()
vgg_conv = VGG16(weights='imagenet', include_top=False, input_shape=(48, 48, 3))


    

In [3]:

    

nTrain = x_train.shape[0]
nVal = y_test.shape[0]
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)


    

In [4]:

    

datagen = ImageDataGenerator(rescale=1./255)
batch_size = 32
 
train_features = np.zeros(shape=(nTrain, 7, 7, 512))
val_features = np.zeros(shape=(nVal, 7, 7, 512))
train_labels = np.zeros(shape=10)


    

In [5]:

    

from  skimage import transform

new_shape = (48,48,3)
x_train = np.asarray([transform.resize(image, new_shape) for image in x_train]) 
x_test  = np.asarray([transform.resize(image, new_shape) for image in x_test ])


    

    




/home/dex/anaconda3/lib/python3.6/site-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.
  warn("The default mode, 'constant', will be changed to 'reflect' in "

In [ ]:

    

# Freeze the layers except the last 4 layers
for layer in vgg_conv.layers[:-4]:
    layer.trainable = False
 
# Check the trainable status of the individual layers
for layer in vgg_conv.layers:
    print(layer, layer.trainable)


    

In [6]:

    

add_model = Sequential()
add_model.add(Flatten(input_shape = vgg_conv.output_shape[1:]))
add_model.add(Dense(256, activation='relu'))
#add_model.add(Dense(1024, activation='relu'))
add_model.add(Dense(10, activation='sigmoid'))

model = Model(inputs=vgg_conv.input, outputs=add_model(vgg_conv.output))
model.compile(loss='binary_crossentropy', optimizer = optimizers.SGD(lr=1e-4, momentum=0.9),
              metrics=['accuracy'])

model.summary()


    

    




_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 48, 48, 3)         0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 48, 48, 64)        1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 48, 48, 64)        36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 24, 24, 64)        0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 24, 24, 128)       73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 24, 24, 128)       147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 12, 12, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 12, 12, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 12, 12, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 12, 12, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 6, 6, 256)         0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 6, 6, 512)         1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 6, 6, 512)         2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 6, 6, 512)         2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 3, 3, 512)         0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 3, 3, 512)         2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 3, 3, 512)         2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 3, 3, 512)         2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         
_________________________________________________________________
sequential_1 (Sequential)    (None, 10)                133898    
=================================================================
Total params: 14,848,586
Trainable params: 14,848,586
Non-trainable params: 0
_________________________________________________________________

In [9]:

    

lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
                               cooldown=0,
                               patience=5,
                               min_lr=0.5e-6)
callbacks = [ lr_reducer, PlotLossesKeras()]


    

In [11]:

    

x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)


    

In [14]:

    

y_train.shape, x_test.shape, x_test.shape, y_test.shape


    

    
Out[14]:





((50000, 10), (10000, 48, 48, 3), (10000, 48, 48, 3), (10000, 10))

In [21]:

    

for i in datagen.flow(x_train, y_train, batch_size=batch_size):
    print (type(i), i[0].shape, i[1].shape)
    break


    

    




<class 'tuple'> (32, 48, 48, 3) (32, 10)

In [22]:

    

datagen.fit(x_train)
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
              epochs=20, steps_per_epoch=x_train.shape[0] // batch_size,
              validation_data=(x_test, y_test),
              shuffle=True, callbacks = callbacks)


    

    













    




Epoch 14/20
1561/1562 [============================>.] - ETA: 0s - loss: 0.3251 - acc: 0.9000





    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-22-9a23cc631212> in <module>()
      3               epochs=20, steps_per_epoch=x_train.shape[0] // batch_size,
      4               validation_data=(x_test, y_test),
----> 5               shuffle=True, callbacks = callbacks)

~/anaconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name +
     90                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py 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)
   1424             use_multiprocessing=use_multiprocessing,
   1425             shuffle=shuffle,
-> 1426             initial_epoch=initial_epoch)
   1427 
   1428     @interfaces.legacy_generator_methods_support

~/anaconda3/lib/python3.6/site-packages/keras/engine/training_generator.py 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)
    217                             batch_size=batch_size,
    218                             sample_weight=val_sample_weights,
--> 219                             verbose=0)
    220                     if not isinstance(val_outs, list):
    221                         val_outs = [val_outs]

~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps)
   1114                                          batch_size=batch_size,
   1115                                          verbose=verbose,
-> 1116                                          steps=steps)
   1117 
   1118     def predict(self, x,

~/anaconda3/lib/python3.6/site-packages/keras/engine/training_arrays.py in test_loop(model, f, ins, batch_size, verbose, steps)
    396                 ins_batch[i] = ins_batch[i].toarray()
    397 
--> 398             batch_outs = f(ins_batch)
    399             if isinstance(batch_outs, list):
    400                 if batch_index == 0:

~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
   2659                 return self._legacy_call(inputs)
   2660 
-> 2661             return self._call(inputs)
   2662         else:
   2663             if py_any(is_tensor(x) for x in inputs):

~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in _call(self, inputs)
   2629                                 symbol_vals,
   2630                                 session)
-> 2631         fetched = self._callable_fn(*array_vals)
   2632         return fetched[:len(self.outputs)]
   2633 

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in __call__(self, *args)
   1449         if self._session._created_with_new_api:
   1450           return tf_session.TF_SessionRunCallable(
-> 1451               self._session._session, self._handle, args, status, None)
   1452         else:
   1453           return tf_session.TF_DeprecatedSessionRunCallable(

KeyboardInterrupt: 

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