def test_fit_octave_conv_low(self):
inputs = Input(shape=(32, 3))
conv = octave_conv_1d(inputs, filters=13, kernel_size=3)
pool = octave_dual(conv, MaxPool1D())
conv = octave_conv_1d(pool, filters=7, kernel_size=3, name='Mid')
pool = octave_dual(conv, MaxPool1D())
conv = octave_conv_1d(pool, filters=5, kernel_size=3, ratio_out=1.0)
flatten = octave_dual(conv, Flatten())
outputs = Dense(units=2, activation='softmax')(flatten)
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model.summary(line_length=200)
self._test_fit(model)
def test_make_dual_layer(self):
inputs = Input(shape=(32, 3))
conv = OctaveConv1D(13, kernel_size=3)(inputs)
pool = octave_dual(conv, MaxPool1D())
conv = OctaveConv1D(7, kernel_size=3)(pool)
pool = octave_dual(conv, MaxPool1D())
conv = OctaveConv1D(5, kernel_size=3, ratio_out=0.0)(pool)
flatten = octave_dual(conv, Flatten())
outputs = Dense(units=2, activation='softmax')(flatten)
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model.summary(line_length=200)
self._test_fit(model)
def test_fit_octave_conv_high(self):
inputs = keras.layers.Input(shape=(32, 32, 3))
conv = octave_conv_2d(inputs, filters=13, kernel_size=3)
pool = octave_dual(conv, keras.layers.MaxPool2D())
conv = octave_conv_2d(pool,
filters=7,
kernel_size=3,
name='Octave-Mid')
pool = octave_dual(conv, keras.layers.MaxPool2D())
conv = octave_conv_2d(pool, filters=5, kernel_size=3, ratio_out=0.0)
flatten = keras.layers.Flatten()(conv)
outputs = keras.layers.Dense(units=2, activation='softmax')(flatten)
model = keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model.summary(line_length=200)
self._test_fit(model)
