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robstewart57/keras-1.2.2-mnist-weights.txt

Created May 14, 2019 18:04
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Mnist output with Keras 1.2.2 and 2.2.4
Raw
keras-1.2.2-mnist-weights.txt
Using Theano backend.
('X_train shape:', (60000, 1, 28, 28))
(60000, 'train samples')
(10000, 'test samples')
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
convolution2d_1 (Convolution2D) (None, 32, 26, 26) 320 convolution2d_input_1[0][0]
____________________________________________________________________________________________________
activation_1 (Activation) (None, 32, 26, 26) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 32, 24, 24) 9248 activation_1[0][0]
____________________________________________________________________________________________________
activation_2 (Activation) (None, 32, 24, 24) 0 convolution2d_2[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 32, 12, 12) 0 activation_2[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 4608) 0 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 128) 589952 flatten_1[0][0]
____________________________________________________________________________________________________
activation_3 (Activation) (None, 128) 0 dense_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 10) 1290 activation_3[0][0]
____________________________________________________________________________________________________
activation_4 (Activation) (None, 10) 0 dense_2[0][0]
====================================================================================================
Total params: 600,810
Trainable params: 600,810
Non-trainable params: 0
____________________________________________________________________________________________________
Model loaded! Testing.........
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[]
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[]
Loaded model test accuracy: 99.08%
Raw
keras-2.2.4-mnist-weights.txt
Using Theano backend.
X_train shape: (60000, 1, 28, 28)
60000 train samples
10000 test samples
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 32, 26, 26) 320
_________________________________________________________________
activation_1 (Activation) (None, 32, 26, 26) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 32, 24, 24) 9248
_________________________________________________________________
activation_2 (Activation) (None, 32, 24, 24) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 32, 12, 12) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 4608) 0
_________________________________________________________________
dense_1 (Dense) (None, 128) 589952
_________________________________________________________________
activation_3 (Activation) (None, 128) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 1290
_________________________________________________________________
activation_4 (Activation) (None, 10) 0
=================================================================
Total params: 600,810
Trainable params: 600,810
Non-trainable params: 0
_________________________________________________________________
Model loaded! Testing.........
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-0.00104359, -0.04893014, -0.00031316, -0.01240131, -0.02536447,
-0.03595945, -0.00600472, -0.00021108, -0.02609218, -0.03892738,
-0.05018377, -0.06270373, -0.02629877, -0.02588009, -0.02463793,
-0.00830029, -0.03068862, -0.00635177], dtype=float32)]
[]
[array([[-0.22294168, 0.16472656, -0.20760794, ..., -0.29669467,
-0.20647448, -0.00237487],
[ 0.11112173, -0.07298024, 0.1263329 , ..., 0.16448805,
-0.21744893, -0.21255432],
[-0.04881252, -0.08479621, 0.07850827, ..., -0.05121636,
0.14358447, 0.13019344],
...,
[-0.20977515, 0.19680858, -0.11731596, ..., -0.19713129,
-0.2711569 , 0.15835182],
[ 0.09318311, -0.03883193, 0.00718685, ..., -0.00452011,
0.10841092, 0.11810151],
[ 0.00539326, 0.17449129, 0.14923239, ..., -0.08515216,
-0.14880708, -0.173281 ]], dtype=float32), array([-0.04242039, 0.04178922, -0.03102371, 0.03539446, -0.03191667,
-0.04334453, -0.13229504, -0.01163885, 0.03705839, 0.08497411],
dtype=float32)]
[]
Loaded model test accuracy: 12.78%
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