])(x)
input_T = Reshape((3, 3))(x)
in_0 = MatMul()([input_points, input_T])
# forward net0
f_0 = Conv1D(64, 1, activation='relu')(in_0)
f_0 = BatchNormalization()(f_0)
f_0 = Conv1D(64, 1, activation='relu')(f_0)
c_0 = Conv1D(256, 1, activation='relu')(in_0)
c_0 = BatchNormalization()(c_0)
c_0 = Conv1D(256, 1, activation='relu')(c_0)
i_0 = GumbelSoftmax(temperature=1, hard=True)(c_0)
for _ in range(2):
i_0_t = GumbelSoftmax(temperature=1, hard=True)(c_0)
i_0 = GumbelIntegration('max')([i_0, i_0_t])
out_0 = GumbelPooling(pool_way='max')([f_0, i_0])
''''''
global_feature = MaxPooling1D(pool_size=256)(out_0)
c = Dense(512, activation='relu')(global_feature)
c = BatchNormalization()(c)
c = Dropout(0.5)(c)
c = Dense(256, activation='relu')(c)
c = BatchNormalization()(c)
c = Dropout(0.5)(c)
c = Dense(40, activation='softmax')(c)
prediction = Flatten()(c)
'''
model = Model(inputs=input_points, outputs=[prediction])
xx = np.random.rand(32,2048, 3) - 0.5
y = model.predict_on_batch(xx)
'''
np.eye(64).flatten().astype(np.float32)
])(f)
feature_T0 = Reshape((64, 64))(f)
in_0 = MatMul()([g, feature_T0])
# forward net0
f_0 = Conv1D(64, 1, activation='relu')(in_0)
f_0 = BatchNormalization()(f_0)
f_0 = Conv1D(64, 1, activation='relu')(f_0)
f_0 = BatchNormalization()(f_0)
c_0 = Conv1D(128, 1, activation='relu')(in_0)
c_0 = BatchNormalization()(c_0)
c_0 = Conv1D(128, 1, activation='relu')(c_0)
c_0 = BatchNormalization()(c_0)
i_0 = Activation('hard_sigmoid')(c_0)
out_0 = GumbelPooling(pool_way='mean')([f_0, i_0])
# feature transform net
in_1 = Conv1D(64, 1, activation='relu')(out_0)
in_1 = BatchNormalization()(in_1)
in_1 = Conv1D(128, 1, activation='relu')(in_1)
in_1 = BatchNormalization()(in_1)
in_1 = Conv1D(1024, 1, activation='relu')(in_1)
in_1 = BatchNormalization()(in_1)
in_1 = MaxPooling1D(pool_size=128)(in_1)
in_1 = Dense(512, activation='relu')(in_1)
in_1 = BatchNormalization()(in_1)
in_1 = Dense(256, activation='relu')(in_1)
in_1 = BatchNormalization()(in_1)
in_1 = Dense(64 * 64,
weights=[