82 lines
1.7 KiB
Python
82 lines
1.7 KiB
Python
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from tensorflow.keras import layers as kl
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from tensorflow.keras import models as km
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from tensorflow.keras import losses as ks
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from tensorflow.keras import optimizers as ko
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from tensorflow.keras import callbacks as kc
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def conv(i, k, s, a = None, t = (1, 1)):
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l1 = kl.Conv2D(k, s, strides = t, padding = 'same')(i)
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l2 = kl.BatchNormalization()(l1)
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if a:
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return kl.Activation(a)(l2)
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else:
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return l2
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def sepc(i, k, s, a = None, t = (1, 1)):
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l1 = kl.SeparableConv2D(k, s, strides = t, padding = 'same')(i)
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l2 = kl.BatchNormalization()(l1)
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if a:
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return kl.Activation(a)(l2)
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else:
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return l2
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def maxp(i, s):
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return kl.MaxPooling2D(s, padding = 'same')(i)
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def addl(*i):
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return kl.Add()([*i])
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def gapl(i):
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return kl.GlobalAveragePooling2D()(i)
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def drpt(i, r):
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return kl.Dropout(r)(i)
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def dnsl(i, s):
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return kl.Dense(s)(i)
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def rscl(i, r):
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return kl.Rescaling(r)(i)
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def raise_xception(si = (300, 300), cc = 1):
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i = kl.Input(shape = (*si, 3))
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b01 = rscl(i, 1/255)
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b11 = conv(b01, 16, (3,3), 'relu')
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b21 = conv(b11, 32, (1,1), t = (2,2))
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b22 = sepc(b11, 32, (3,3), 'relu')
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b23 = maxp(b22, (2,2))
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b31 = addl(b21, b23)
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b41 = conv(b31, 64, (1,1), t = (2,2))
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b42 = sepc(b31, 64, (3,3), 'relu')
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b43 = maxp(b42, (2,2))
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b51 = addl(b41, b43)
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b61 = sepc(b51, 128, (3,3))
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b71 = gapl(b61)
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b81 = drpt(b71, 0.3)
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b91 = dnsl(b81, 64)
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o = dnsl(b91, cc)
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return km.Model(inputs = i, outputs = o)
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m = raise_xception()
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m.compile(
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optimizer = ko.Lion(learning_rate = 0.002),
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loss = ks.BinaryCrossentropy(from_logits = True),
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metrics = ['accuracy'])
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m.summary()
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