<jupyter_start><jupyter_text>MNIST results using kerasI trained two networks on the mnsit dataset. The networks used different arcitechture and I trained both for 12 epochs to see the results.  - A simple network with a convolutional layer and a dense layer.  - A complex network with two convolutional layers, a maxpooling layer, a dropout layer, and a dense layer.  Structure of the simple network<jupyter_code>model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
				 activation='relu',
				 input_shape=input_shape))
model.add(Flatten())
model.add(Dense(num_classes, activation='softmax'))
model.summary()<jupyter_output>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_8 (Conv2D)            (None, 26, 26, 32)        320       
_________________________________________________________________
flatten_7 (Flatten)          (None, 21632)             0         
_________________________________________________________________
dense_7 (Dense)              (None, 10)                216330    
=================================================================
Total params: 216,650
Trainable params: 216,650
Non-trainable params: 0
_________________________________________________________________<jupyter_text>Simple network results<jupyter_code>train(model)<jupyter_output>Train on 60000 samples, validate on 10000 samples
Epoch 1/12
60000/60000 [==============================] - 29s 488us/step - loss: 0.2959 - acc: 0.9137 - val_loss: 0.1297 - val_acc: 0.9634
Epoch 2/12
60000/60000 [==============================] - 29s 482us/step - loss: 0.1092 - acc: 0.9696 - val_loss: 0.0887 - val_acc: 0.9727
Epoch 3/12
60000/60000 [==============================] - 29s 478us/step - loss: 0.0778 - acc: 0.9783 - val_loss: 0.0854 - val_acc: 0.9725
Epoch 4/12
60000/60000 [==============================] - 28s 468us/step - loss: 0.0627 - acc: 0.9818 - val_loss: 0.0642 - val_acc: 0.9810
Epoch 5/12
60000/60000 [==============================] - 28s 465us/step - loss: 0.0541 - acc: 0.9844 - val_loss: 0.0591 - val_acc: 0.9816
Epoch 6/12
60000/60000 [==============================] - 30s 508us/step - loss: 0.0476 - acc: 0.9862 - val_loss: 0.0565 - val_acc: 0.9826
Epoch 7/12
60000/60000 [==============================] - 35s 583us/step - loss: 0.0430 - acc: 0.9872 - val_loss: 0.[...]<jupyter_text>Structure of the complex network<jupyter_code>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(num_classes, activation='softmax'))
model.summary()<jupyter_output>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_13 (Conv2D)           (None, 26, 26, 32)        320       
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 24, 24, 64)        18496     
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 12, 12, 64)        0         
_________________________________________________________________
dropout_4 (Dropout)          (None, 12, 12, 64)        0         
_________________________________________________________________
flatten_10 (Flatten)         (None, 9216)              0         
_________________________________________________________________
dense_10 (Dense)             (None, 10)                92170     
=================================================================
Total para[...]<jupyter_text>Complex network results<jupyter_code>train(model)<jupyter_output>Train on 60000 samples, validate on 10000 samples
Epoch 1/12
60000/60000 [==============================] - 117s 2ms/step - loss: 0.2262 - acc: 0.9306 - val_loss: 0.0586 - val_acc: 0.9808
Epoch 2/12
60000/60000 [==============================] - 149s 2ms/step - loss: 0.0673 - acc: 0.9800 - val_loss: 0.0453 - val_acc: 0.9845
Epoch 3/12
60000/60000 [==============================] - 150s 2ms/step - loss: 0.0514 - acc: 0.9849 - val_loss: 0.0426 - val_acc: 0.9862
Epoch 4/12
60000/60000 [==============================] - 118s 2ms/step - loss: 0.0430 - acc: 0.9869 - val_loss: 0.0357 - val_acc: 0.9886
Epoch 5/12
60000/60000 [==============================] - 120s 2ms/step - loss: 0.0379 - acc: 0.9886 - val_loss: 0.0399 - val_acc: 0.9865
Epoch 6/12
60000/60000 [==============================] - 122s 2ms/step - loss: 0.0327 - acc: 0.9904 - val_loss: 0.0368 - val_acc: 0.9877
Epoch 7/12
60000/60000 [==============================] - 117s 2ms/step - loss: 0.0305 - acc: 0.9906 - val_loss: 0.0361 - [...]
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