3.4. Face RecognitionΒΆ
In [1]:
import conx as cx
Using Theano backend.
Conx, version 3.6.0
In [2]:
faces = cx.Dataset.get('cmu_faces_half_size')
In [3]:
faces.shuffle()
cx.view(faces.inputs[0:12], layout=(2,6), scale=2)
In [4]:
cx.view(faces.inputs[0])
In [5]:
faces.targets[0]
Out[5]:
[0, 0, 0, 1]
In [6]:
cx.shape(faces.inputs[0])
Out[6]:
(60, 64)
In [7]:
net = cx.Network("Pose Recognizer")
net.add(cx.Layer('input', cx.shape(faces.inputs[0])),
cx.FlattenLayer("flatten"),
cx.Layer('hidden', 3, activation='sigmoid'),
cx.Layer('output', 4, activation='sigmoid'))
net.connect()
net.compile(loss='mean_squared_error', optimizer="SGD", lr=0.2, momentum=0.1)
In [8]:
net.set_dataset(faces)
faces.split(0.30) # reserve 30% of input data for testing set
faces.summary()
_________________________________________________________________
CMU Faces, half-size:
Patterns Shape Range
=================================================================
inputs (60, 64) (0.0, 1.0)
targets (4,) (0, 1)
=================================================================
Total patterns: 624
Training patterns: 437
Testing patterns: 187
_________________________________________________________________
In [9]:
net.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) (None, 60, 64) 0
_________________________________________________________________
flatten (Flatten) (None, 3840) 0
_________________________________________________________________
hidden (Dense) (None, 3) 11523
_________________________________________________________________
output (Dense) (None, 4) 16
=================================================================
Total params: 11,539
Trainable params: 11,539
Non-trainable params: 0
_________________________________________________________________
In [10]:
net.propagate(faces.inputs[3])
Out[10]:
[0.5489193797111511,
0.41910219192504883,
0.4797622263431549,
0.5765119194984436]
In [11]:
net.dashboard()
In [12]:
net.train(1000, accuracy=1.0, tolerance=0.2, report_rate=5)
========================================================
| Training | Training | Validate | Validate
Epochs | Error | Accuracy | Error | Accuracy
------ | --------- | --------- | --------- | ---------
# 1000 | 0.00750 | 0.97936 | 0.02478 | 0.83511
In [13]:
net.evaluate()
Out[13]:
{'acc': 0.9816933638443935,
'loss': 0.007463851087946163,
'val_acc': 0.8342245976555156,
'val_loss': 0.024856202264520573}
In [14]:
net.plot("all", title="All available metrics")
In [15]:
net.plot(['loss', 'val_loss'], ymax=0.15, start=50, end=150)
In [16]:
net.plot(['.*loss'], ymax=0.15, start=50, end=150)
In [17]:
net.plot_layer_weights('hidden', vshape=faces.inputs.shape[0])
net.plot_layer_weights('output', colormap='viridis')
In [18]:
# colorbar colors or values can be easily changed
net.plot_layer_weights('hidden', vshape=net.dataset.inputs.shape[0],
units=(2,1,0), colormap="RdBu", wrange=(-0.25, 0.25))
net.plot_layer_weights('output', units=0, wmin=0, colormap="copper", ticks=10)
In [19]:
net.show_unit_weights('output', 0)
-6.20 1.68 1.09
In [20]:
net.get_weights("output")
Out[20]:
[[[-6.198843479156494,
0.48533692955970764,
2.2441534996032715,
3.789884567260742],
[1.6767936944961548,
-5.901026248931885,
-1.1506351232528687,
4.787131309509277],
[1.0861774682998657,
4.698576927185059,
-5.883935928344727,
-0.006406568922102451]],
[0.4804971516132355,
-2.6081082820892334,
0.9343416690826416,
-6.336297988891602]]
In [22]:
faces.labels[:10]
Out[22]:
['neil_right_neutral_sunglasses',
'paula_left_angry_eyes',
'bob_up_neutral_sunglasses',
'tom_forward_happy_eyes',
'owen_forward_neutral_sunglasses',
'tom_left_angry_eyes',
'bob_left_angry_sunglasses',
'paula_up_sad_sunglasses',
'alex_up_angry_sunglasses',
'quinn_forward_angry_eyes']