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import keras
import tensorflow as tf

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adadelta

from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer


    

    




Using TensorFlow backend.

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batch_size = 128
num_classes = 10
epochs = 12


    

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(x_train, y_train), (x_test, y_test) = mnist.load_data()

img_rows, img_cols = 28, 28

x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)

input_shape = (img_rows, img_cols, 1)

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


    

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# same thing as applying minmaxscaler in the (0,1) range
x_train = x_train / 255
x_test = x_test / 255


    

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# one hot encoding
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)


    

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with tf.device('/cpu:0'):
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                     activation='relu',
                     input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))
    
    model.compile(loss="categorical_crossentropy",
              optimizer=Adadelta(),
              metrics=['accuracy'])
    
    model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_data=(x_test, y_test))


    

    




Train on 60000 samples, validate on 10000 samples
Epoch 1/12
19840/60000 [========>.....................] - ETA: 69s - loss: 0.6033 - acc: 0.8136





    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-8-3d6aadddba94> in <module>()
     20                     epochs=epochs,
     21                     verbose=1,
---> 22                     validation_data=(x_test, y_test))
     23 

/home/felipe/tf-venv3/lib/python3.5/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)
    851                               class_weight=class_weight,
    852                               sample_weight=sample_weight,
--> 853                               initial_epoch=initial_epoch)
    854 
    855     def evaluate(self, x, y, batch_size=32, verbose=1,

/home/felipe/tf-venv3/lib/python3.5/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)
   1484                               val_f=val_f, val_ins=val_ins, shuffle=shuffle,
   1485                               callback_metrics=callback_metrics,
-> 1486                               initial_epoch=initial_epoch)
   1487 
   1488     def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):

/home/felipe/tf-venv3/lib/python3.5/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)
   1139                 batch_logs['size'] = len(batch_ids)
   1140                 callbacks.on_batch_begin(batch_index, batch_logs)
-> 1141                 outs = f(ins_batch)
   1142                 if not isinstance(outs, list):
   1143                     outs = [outs]

/home/felipe/tf-venv3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
   2101         session = get_session()
   2102         updated = session.run(self.outputs + [self.updates_op],
-> 2103                               feed_dict=feed_dict)
   2104         return updated[:len(self.outputs)]
   2105 

/home/felipe/tf-venv3/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    776     try:
    777       result = self._run(None, fetches, feed_dict, options_ptr,
--> 778                          run_metadata_ptr)
    779       if run_metadata:
    780         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/felipe/tf-venv3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    980     if final_fetches or final_targets:
    981       results = self._do_run(handle, final_targets, final_fetches,
--> 982                              feed_dict_string, options, run_metadata)
    983     else:
    984       results = []

/home/felipe/tf-venv3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1030     if handle is None:
   1031       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1032                            target_list, options, run_metadata)
   1033     else:
   1034       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/felipe/tf-venv3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1037   def _do_call(self, fn, *args):
   1038     try:
-> 1039       return fn(*args)
   1040     except errors.OpError as e:
   1041       message = compat.as_text(e.message)

/home/felipe/tf-venv3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1019         return tf_session.TF_Run(session, options,
   1020                                  feed_dict, fetch_list, target_list,
-> 1021                                  status, run_metadata)
   1022 
   1023     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

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