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KOMAL BADI
Building Autoencoders in Keras
Commits
a9cae3d7
Commit
a9cae3d7
authored
May 14, 2019
by
William Stonewall Monroe
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parent
5fdb6c78
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DenoisingWithAutoencoders.ipynb
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a9cae3d7
%% Cell type:markdown id: tags:
# First lets get our data!
%% Cell type:code id: tags:
```
python
from
keras.datasets
import
mnist
import
numpy
as
np
(
x_train
,
_
),
(
x_test
,
_
)
=
mnist
.
load_data
()
x_train
=
x_train
.
astype
(
'float32'
)
/
255.
x_test
=
x_test
.
astype
(
'float32'
)
/
255.
x_train
=
np
.
reshape
(
x_train
,
(
len
(
x_train
),
28
,
28
,
1
))
# adapt this if using `channels_first` image data format
x_test
=
np
.
reshape
(
x_test
,
(
len
(
x_test
),
28
,
28
,
1
))
# adapt this if using `channels_first` image data format
noise_factor
=
0.5
x_train_noisy
=
x_train
+
noise_factor
*
np
.
random
.
normal
(
loc
=
0.0
,
scale
=
1.0
,
size
=
x_train
.
shape
)
x_test_noisy
=
x_test
+
noise_factor
*
np
.
random
.
normal
(
loc
=
0.0
,
scale
=
1.0
,
size
=
x_test
.
shape
)
x_train_noisy
=
np
.
clip
(
x_train_noisy
,
0.
,
1.
)
x_test_noisy
=
np
.
clip
(
x_test_noisy
,
0.
,
1.
)
```
%%%% Output: stream
Using TensorFlow backend.
%% Cell type:markdown id: tags:
# Let's look at some of our data
%% Cell type:code id: tags:
```
python
# use Matplotlib (don't ask)
import
matplotlib.pyplot
as
plt
##wsm
n
=
10
plt
.
figure
(
figsize
=
(
20
,
2
))
for
i
in
range
(
1
,
n
):
##wsm changed from for i in range(n):
ax
=
plt
.
subplot
(
1
,
n
,
i
)
plt
.
imshow
(
x_test_noisy
[
i
].
reshape
(
28
,
28
))
plt
.
gray
()
ax
.
get_xaxis
().
set_visible
(
False
)
ax
.
get_yaxis
().
set_visible
(
False
)
plt
.
show
()
```
%%%% Output: display_data
%% Cell type:markdown id: tags:
# Let's create our machine learning architecture
Here we're creating an Autoencoder architecture
%% Cell type:code id: tags:
```
python
from
keras.layers
import
Input
,
Dense
,
Conv2D
,
MaxPooling2D
,
UpSampling2D
from
keras.models
import
Model
from
keras
import
backend
as
K
input_img
=
Input
(
shape
=
(
28
,
28
,
1
))
# adapt this if using `channels_first` image data format
x
=
Conv2D
(
32
,
(
3
,
3
),
activation
=
'relu'
,
padding
=
'same'
)(
input_img
)
x
=
MaxPooling2D
((
2
,
2
),
padding
=
'same'
)(
x
)
x
=
Conv2D
(
32
,
(
3
,
3
),
activation
=
'relu'
,
padding
=
'same'
)(
x
)
encoded
=
MaxPooling2D
((
2
,
2
),
padding
=
'same'
)(
x
)
# at this point the representation is (7, 7, 32)
x
=
Conv2D
(
32
,
(
3
,
3
),
activation
=
'relu'
,
padding
=
'same'
)(
encoded
)
x
=
UpSampling2D
((
2
,
2
))(
x
)
x
=
Conv2D
(
32
,
(
3
,
3
),
activation
=
'relu'
,
padding
=
'same'
)(
x
)
x
=
UpSampling2D
((
2
,
2
))(
x
)
decoded
=
Conv2D
(
1
,
(
3
,
3
),
activation
=
'sigmoid'
,
padding
=
'same'
)(
x
)
autoencoder
=
Model
(
input_img
,
decoded
)
autoencoder
.
compile
(
optimizer
=
'adadelta'
,
loss
=
'binary_crossentropy'
)
```
%% Cell type:markdown id: tags:
# Let's Train our Model...
%% Cell type:code id: tags:
```
python
from
keras.callbacks
import
TensorBoard
autoencoder
.
fit
(
x_train_noisy
,
x_train
,
epochs
=
100
,
batch_size
=
128
,
shuffle
=
True
,
validation_data
=
(
x_test_noisy
,
x_test
),
callbacks
=
[
TensorBoard
(
log_dir
=
'/data/scratch/wsmonroe'
,
histogram_freq
=
0
,
write_graph
=
False
)])
```
%%%% Output: stream
Train on 60000 samples, validate on 10000 samples
Epoch 1/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0966 - val_loss: 0.0957
Epoch 2/100
60000/60000 [==============================] - 3s 44us/step - loss: 0.0966 - val_loss: 0.0957
Epoch 3/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0965 - val_loss: 0.0959
Epoch 4/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0963 - val_loss: 0.0959
Epoch 5/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0962 - val_loss: 0.0955
Epoch 6/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0962 - val_loss: 0.0953
Epoch 7/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0962 - val_loss: 0.0953
Epoch 8/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0960 - val_loss: 0.0952
Epoch 9/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0959 - val_loss: 0.0958
Epoch 10/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0959 - val_loss: 0.0957
Epoch 11/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0958 - val_loss: 0.0961
Epoch 12/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0958 - val_loss: 0.0954
Epoch 13/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0957 - val_loss: 0.0952
Epoch 14/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0956 - val_loss: 0.0951
Epoch 15/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0956 - val_loss: 0.0948
Epoch 16/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0956 - val_loss: 0.0949
Epoch 17/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0954 - val_loss: 0.0949
Epoch 18/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0954 - val_loss: 0.0947
Epoch 19/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0953 - val_loss: 0.0947
Epoch 20/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0953 - val_loss: 0.0949
Epoch 21/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0953 - val_loss: 0.0959
Epoch 22/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0952 - val_loss: 0.0952
Epoch 23/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0952 - val_loss: 0.0958
Epoch 24/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0951 - val_loss: 0.0946
Epoch 25/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0952 - val_loss: 0.0947
Epoch 26/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0951 - val_loss: 0.0944
Epoch 27/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0950 - val_loss: 0.0945
Epoch 28/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0950 - val_loss: 0.0947
Epoch 29/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0949 - val_loss: 0.0947
Epoch 30/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0949 - val_loss: 0.0949
Epoch 31/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0948 - val_loss: 0.0954
Epoch 32/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0948 - val_loss: 0.0942
Epoch 33/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0948 - val_loss: 0.0954
Epoch 34/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0948 - val_loss: 0.0942
Epoch 35/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0948 - val_loss: 0.0950
Epoch 36/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0947 - val_loss: 0.0942
Epoch 37/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0947 - val_loss: 0.0945
Epoch 38/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0946 - val_loss: 0.0950
Epoch 39/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0946 - val_loss: 0.0944
Epoch 40/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0946 - val_loss: 0.0943
Epoch 41/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0945 - val_loss: 0.0947
Epoch 42/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0945 - val_loss: 0.0947
Epoch 43/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0945 - val_loss: 0.0952
Epoch 44/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0944 - val_loss: 0.0949
Epoch 45/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0944 - val_loss: 0.0940
Epoch 46/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0944 - val_loss: 0.0940
Epoch 47/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0944 - val_loss: 0.0946
Epoch 48/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0944 - val_loss: 0.0945
Epoch 49/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0943 - val_loss: 0.0945
Epoch 50/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0943 - val_loss: 0.0940
Epoch 51/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0943 - val_loss: 0.0950
Epoch 52/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0943 - val_loss: 0.0939
Epoch 53/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0942 - val_loss: 0.0950
Epoch 54/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0942 - val_loss: 0.0939
Epoch 55/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0942 - val_loss: 0.0940
Epoch 56/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0942 - val_loss: 0.0939
Epoch 57/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0942 - val_loss: 0.0941
Epoch 58/100
60000/60000 [==============================] - 3s 49us/step - loss: 0.0941 - val_loss: 0.0943
Epoch 59/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0941 - val_loss: 0.0948
Epoch 60/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0941 - val_loss: 0.0937
Epoch 61/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0940 - val_loss: 0.0937
Epoch 62/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0941 - val_loss: 0.0946
Epoch 63/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0940 - val_loss: 0.0944
Epoch 64/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0940 - val_loss: 0.0938
Epoch 65/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0940 - val_loss: 0.0938
Epoch 66/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0940 - val_loss: 0.0937
Epoch 67/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0939 - val_loss: 0.0937
Epoch 68/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0939 - val_loss: 0.0937
Epoch 69/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0939 - val_loss: 0.0944
Epoch 70/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0939 - val_loss: 0.0938
Epoch 71/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0940 - val_loss: 0.0940
Epoch 72/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0939 - val_loss: 0.0937
Epoch 73/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0939 - val_loss: 0.0941
Epoch 74/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0939 - val_loss: 0.0936
Epoch 75/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0938 - val_loss: 0.0936
Epoch 76/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0938 - val_loss: 0.0937
Epoch 77/100
%%%% Output: stream
60000/60000 [==============================] - 3s 50us/step - loss: 0.0939 - val_loss: 0.0936
Epoch 78/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0938 - val_loss: 0.0937
Epoch 79/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0938 - val_loss: 0.0935
Epoch 80/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0938 - val_loss: 0.0936
Epoch 81/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0937 - val_loss: 0.0935
Epoch 82/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0937 - val_loss: 0.0936
Epoch 83/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0937 - val_loss: 0.0935
Epoch 84/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0937 - val_loss: 0.0935
Epoch 85/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0937 - val_loss: 0.0936
Epoch 86/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0937 - val_loss: 0.0935
Epoch 87/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0936 - val_loss: 0.0944
Epoch 88/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0937 - val_loss: 0.0942
Epoch 89/100
60000/60000 [==============================] - 3s 45us/step - loss: 0.0936 - val_loss: 0.0934
Epoch 90/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0936 - val_loss: 0.0936
Epoch 91/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0936 - val_loss: 0.0935
Epoch 92/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0936 - val_loss: 0.0936
Epoch 93/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0936 - val_loss: 0.0940
Epoch 94/100
60000/60000 [==============================] - 3s 48us/step - loss: 0.0936 - val_loss: 0.0940
Epoch 95/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0935 - val_loss: 0.0934
Epoch 96/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0936 - val_loss: 0.0934
Epoch 97/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0936 - val_loss: 0.0944
Epoch 98/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0936 - val_loss: 0.0933
Epoch 99/100
60000/60000 [==============================] - 3s 46us/step - loss: 0.0935 - val_loss: 0.0936
Epoch 100/100
60000/60000 [==============================] - 3s 47us/step - loss: 0.0935 - val_loss: 0.0934
%%%% Output: execute_result
<keras.callbacks.History at 0x2aab68566470>
%% Cell type:markdown id: tags:
# How'd we do?
%% Cell type:code id: tags:
```
python
decoded_imgs
=
autoencoder
.
predict
(
x_test_noisy
)
n
=
10
plt
.
figure
(
figsize
=
(
20
,
4
))
for
i
in
range
(
1
,
n
):
# display original
ax
=
plt
.
subplot
(
2
,
n
,
i
)
plt
.
imshow
(
x_test_noisy
[
i
].
reshape
(
28
,
28
))
plt
.
gray
()
ax
.
get_xaxis
().
set_visible
(
False
)
ax
.
get_yaxis
().
set_visible
(
False
)
# display reconstruction
ax
=
plt
.
subplot
(
2
,
n
,
i
+
n
)
plt
.
imshow
(
decoded_imgs
[
i
].
reshape
(
28
,
28
))
plt
.
gray
()
ax
.
get_xaxis
().
set_visible
(
False
)
ax
.
get_yaxis
().
set_visible
(
False
)
plt
.
show
()
```
%%%% Output: display_data
%% Cell type:code id: tags:
```
python
``
`
%%
Cell
type
:
code
id
:
tags
:
```
python
```
...
...
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