VGGNet in Keras

In [1]:

    

import numpy as np
np.random.seed(42)


    

In [2]:

    

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.callbacks import TensorBoard # for part 3.5 on TensorBoard


    

    




Using TensorFlow backend.

In [3]:

    

import tflearn.datasets.oxflower17 as oxflower17
X, Y = oxflower17.load_data(one_hot=True)


    

In [4]:

    

model = Sequential()

model.add(Conv2D(64, 3, activation='relu', input_shape=(224, 224, 3)))
model.add(Conv2D(64, 3, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(BatchNormalization())

model.add(Conv2D(128, 3, activation='relu'))
model.add(Conv2D(128, 3, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(BatchNormalization())

model.add(Conv2D(256, 3, activation='relu'))
model.add(Conv2D(256, 3, activation='relu'))
model.add(Conv2D(256, 3, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(BatchNormalization())

model.add(Conv2D(512, 3, activation='relu'))
model.add(Conv2D(512, 3, activation='relu'))
model.add(Conv2D(512, 3, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(BatchNormalization())

model.add(Conv2D(512, 3, activation='relu'))
model.add(Conv2D(512, 3, activation='relu'))
model.add(Conv2D(512, 3, activation='relu'))
model.add(MaxPooling2D(2, 2))
model.add(BatchNormalization())

model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))

model.add(Dense(17, activation='softmax'))


    

In [5]:

    

model.summary()


    

    




_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 222, 222, 64)      1792      
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 220, 220, 64)      36928     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 110, 110, 64)      0         
_________________________________________________________________
batch_normalization_1 (Batch (None, 110, 110, 64)      256       
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 108, 108, 128)     73856     
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 106, 106, 128)     147584    
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 53, 53, 128)       0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 53, 53, 128)       512       
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 51, 51, 256)       295168    
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 49, 49, 256)       590080    
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 47, 47, 256)       590080    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 23, 23, 256)       0         
_________________________________________________________________
batch_normalization_3 (Batch (None, 23, 23, 256)       1024      
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 21, 21, 512)       1180160   
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 19, 19, 512)       2359808   
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 17, 17, 512)       2359808   
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 8, 8, 512)         0         
_________________________________________________________________
batch_normalization_4 (Batch (None, 8, 8, 512)         2048      
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 6, 6, 512)         2359808   
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 4, 4, 512)         2359808   
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 2, 2, 512)         2359808   
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 1, 1, 512)         0         
_________________________________________________________________
batch_normalization_5 (Batch (None, 1, 1, 512)         2048      
_________________________________________________________________
flatten_1 (Flatten)          (None, 512)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 4096)              2101248   
_________________________________________________________________
dropout_1 (Dropout)          (None, 4096)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 4096)              16781312  
_________________________________________________________________
dropout_2 (Dropout)          (None, 4096)              0         
_________________________________________________________________
dense_3 (Dense)              (None, 17)                69649     
=================================================================
Total params: 33,672,785
Trainable params: 33,669,841
Non-trainable params: 2,944
_________________________________________________________________

In [6]:

    

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])


    

In [7]:

    

# tensorbrd = TensorBoard('logs/vggnet')


    

In [9]:

    

model.fit(X, Y, batch_size=64, epochs=250, verbose=1, validation_split=0.1, shuffle=True) # callbacks=[tensorbrd])


    

    




Train on 1224 samples, validate on 136 samples
Epoch 1/250
 192/1224 [===>..........................] - ETA: 494s - loss: 3.7027 - acc: 0.0938





    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-9-56736a14b756> in <module>()
----> 1 model.fit(X, Y, batch_size=64, epochs=250, verbose=1, validation_split=0.1, shuffle=True) # callbacks=[tensorbrd])

/opt/conda/lib/python3.6/site-packages/keras/models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
    861                               class_weight=class_weight,
    862                               sample_weight=sample_weight,
--> 863                               initial_epoch=initial_epoch)
    864 
    865     def evaluate(self, x, y, batch_size=32, verbose=1,

/opt/conda/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
   1428                               val_f=val_f, val_ins=val_ins, shuffle=shuffle,
   1429                               callback_metrics=callback_metrics,
-> 1430                               initial_epoch=initial_epoch)
   1431 
   1432     def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):

/opt/conda/lib/python3.6/site-packages/keras/engine/training.py in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
   1077                 batch_logs['size'] = len(batch_ids)
   1078                 callbacks.on_batch_begin(batch_index, batch_logs)
-> 1079                 outs = f(ins_batch)
   1080                 if not isinstance(outs, list):
   1081                     outs = [outs]

/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
   2266         updated = session.run(self.outputs + [self.updates_op],
   2267                               feed_dict=feed_dict,
-> 2268                               **self.session_kwargs)
   2269         return updated[:len(self.outputs)]
   2270 

/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     if final_fetches or final_targets:
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:
    967       results = []

/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013     if handle is None:
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:
   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1020   def _do_call(self, fn, *args):
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:
   1024       message = compat.as_text(e.message)

/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1002         return tf_session.TF_Run(session, options,
   1003                                  feed_dict, fetch_list, target_list,
-> 1004                                  status, run_metadata)
   1005 
   1006     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

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