In [1]:
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
Using TensorFlow backend.
In [2]:
(X_train, y_train), (X_test, y_test) = mnist.load_data()
In [3]:
X_train.shape
Out[3]:
(60000, 28, 28)
In [4]:
y_train.shape
Out[4]:
(60000,)
In [5]:
y_train[0:100]
Out[5]:
array([5, 0, 4, 1, 9, 2, 1, 3, 1, 4, 3, 5, 3, 6, 1, 7, 2, 8, 6, 9, 4, 0, 9,
1, 1, 2, 4, 3, 2, 7, 3, 8, 6, 9, 0, 5, 6, 0, 7, 6, 1, 8, 7, 9, 3, 9,
8, 5, 9, 3, 3, 0, 7, 4, 9, 8, 0, 9, 4, 1, 4, 4, 6, 0, 4, 5, 6, 1, 0,
0, 1, 7, 1, 6, 3, 0, 2, 1, 1, 7, 9, 0, 2, 6, 7, 8, 3, 9, 0, 4, 6, 7,
4, 6, 8, 0, 7, 8, 3, 1], dtype=uint8)
In [6]:
X_train[0]
Out[6]:
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
18, 18, 18, 126, 136, 175, 26, 166, 255, 247, 127, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170,
253, 253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253,
253, 253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253,
253, 198, 182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253,
205, 11, 0, 43, 154, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 154, 253,
90, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253,
190, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190,
253, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35,
241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39,
148, 229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221,
253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253,
253, 253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253,
195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133,
11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0]], dtype=uint8)
In [7]:
X_train = X_train.reshape(60000, 784).astype('float32')
X_test = X_test.reshape(10000, 784).astype('float32')
In [8]:
X_train /= 255
X_test /= 255
In [9]:
n_classes = 10
y_train = keras.utils.to_categorical(y_train, n_classes)
y_test = keras.utils.to_categorical(y_test, n_classes)
In [10]:
X_train[0]
Out[10]:
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0.07058824, 0.49411765, 0.53333336, 0.68627453, 0.10196079,
0.65098041, 1. , 0.96862745, 0.49803922, 0. ,
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0. , 0.11764706, 0.14117648, 0.36862746, 0.60392159,
0.66666669, 0.99215686, 0.99215686, 0.99215686, 0.99215686,
0.99215686, 0.88235295, 0.67450982, 0.99215686, 0.94901961,
0.7647059 , 0.25098041, 0. , 0. , 0. ,
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0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686,
0.99215686, 0.99215686, 0.99215686, 0.98431373, 0.36470589,
0.32156864, 0.32156864, 0.21960784, 0.15294118, 0. ,
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0. , 0.07058824, 0.85882354, 0.99215686, 0.99215686,
0.99215686, 0.99215686, 0.99215686, 0.7764706 , 0.71372551,
0.96862745, 0.94509804, 0. , 0. , 0. ,
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0.80392158, 0.04313726, 0. , 0.16862746, 0.60392159,
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0.00392157, 0.60392159, 0.99215686, 0.35294119, 0. ,
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0.99215686, 0.74509805, 0.00784314, 0. , 0. ,
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0.42352942, 0.00392157, 0. , 0. , 0. ,
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0.72941178, 0.99215686, 0.99215686, 0.58823532, 0.10588235,
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0.98823529, 0.99215686, 0.73333335, 0. , 0. ,
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0.97647059, 0.25098041, 0. , 0. , 0. ,
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0.71764708, 0.99215686, 0.99215686, 0.81176472, 0.00784314,
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0.98039216, 0.71372551, 0. , 0. , 0. ,
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0.99215686, 0.99215686, 0.99215686, 0.7764706 , 0.31764707,
0.00784314, 0. , 0. , 0. , 0. ,
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0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7647059 ,
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In [11]:
y_train[0]
Out[11]:
array([ 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.])
In [12]:
model = Sequential()
model.add(Dense(64, activation='sigmoid', input_shape=(784,)))
model.add(Dense(10, activation='softmax'))
In [13]:
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 64) 50240
_________________________________________________________________
dense_2 (Dense) (None, 10) 650
=================================================================
Total params: 50,890
Trainable params: 50,890
Non-trainable params: 0
_________________________________________________________________
In [14]:
(64*784)+64
Out[14]:
50240
In [15]:
(10*64)+10
Out[15]:
650
In [16]:
model.compile(loss='mean_squared_error', optimizer=SGD(lr=0.01), metrics=['accuracy'])
In [17]:
model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=128, epochs=10, verbose=1)
Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 [==============================] - 1s - loss: 0.0916 - acc: 0.1020 - val_loss: 0.0911 - val_acc: 0.1192
Epoch 2/10
60000/60000 [==============================] - 1s - loss: 0.0907 - acc: 0.1389 - val_loss: 0.0904 - val_acc: 0.1621
Epoch 3/10
60000/60000 [==============================] - 1s - loss: 0.0901 - acc: 0.1754 - val_loss: 0.0898 - val_acc: 0.1956
Epoch 4/10
60000/60000 [==============================] - 1s - loss: 0.0896 - acc: 0.1995 - val_loss: 0.0894 - val_acc: 0.2190
Epoch 5/10
60000/60000 [==============================] - 1s - loss: 0.0892 - acc: 0.2267 - val_loss: 0.0890 - val_acc: 0.2546
Epoch 6/10
60000/60000 [==============================] - 1s - loss: 0.0888 - acc: 0.2712 - val_loss: 0.0886 - val_acc: 0.2963
Epoch 7/10
60000/60000 [==============================] - 1s - loss: 0.0885 - acc: 0.3150 - val_loss: 0.0882 - val_acc: 0.3321
Epoch 8/10
60000/60000 [==============================] - 1s - loss: 0.0881 - acc: 0.3444 - val_loss: 0.0879 - val_acc: 0.3585
Epoch 9/10
60000/60000 [==============================] - 1s - loss: 0.0878 - acc: 0.3640 - val_loss: 0.0876 - val_acc: 0.3755
Epoch 10/10
60000/60000 [==============================] - 1s - loss: 0.0875 - acc: 0.3800 - val_loss: 0.0873 - val_acc: 0.3901
Out[17]:
<keras.callbacks.History at 0x7fb4cd6e6320>
In [ ]:
Content source: the-deep-learners/nyc-ds-academy
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