def create_model(): from elektronn2 import neuromancer as nm in_sh = (None, 1, 11, 155, 155) inp = nm.Input(in_sh, 'b,f,z,x,y', name='raw') out = nm.Conv(inp, 20, (1, 4, 4), (1, 2, 2)) out = nm.Conv(out, 40, (3, 3, 3), (1, 2, 2)) out = nm.Conv(out, 150, (2, 4, 4), (2, 1, 1)) out = nm.Conv(out, 200, (1, 3, 3)) out = nm.Conv(out, 200, (1, 3, 3)) out = nm.Conv(out, 200, (1, 1, 1)) out = nm.Conv(out, 2, (1, 1, 1), activation_func='lin') probs = nm.Softmax(out) target = nm.Input_like(probs, override_f=1, name='target') loss_pix = nm.MultinoulliNLL(probs, target, target_is_sparse=True) loss = nm.AggregateLoss(loss_pix, name='loss') errors = nm.Errors(probs, target, target_is_sparse=True) model = nm.model_manager.getmodel() model.designate_nodes(input_node=inp, target_node=target, loss_node=loss, prediction_node=probs, prediction_ext=[loss, errors, probs]) return model
def create_model(): from elektronn2 import neuromancer in_sh = (None, 1, 111+16*5, 111+16*5, 13+4*5) inp = neuromancer.Input(in_sh, 'b,f,x,y,z', name='raw') out = neuromancer.Conv(inp, 12, (6, 6, 1), (2, 2, 1)) out = neuromancer.Conv(out, 24, (4, 4, 1), (2, 2, 1)) out = neuromancer.Conv(out, 36, (4, 4, 4), (2, 2, 2)) out = neuromancer.Conv(out, 48, (4, 4, 2), (2, 2, 2)) out = neuromancer.Conv(out, 48, (4, 4, 2), (1, 1, 1)) out = neuromancer.Conv(out, 4, (1, 1, 1), (1, 1, 1), activation_func='lin', name='sy') probs = neuromancer.Softmax(out) target = neuromancer.Input_like(probs, override_f=1, name='target') loss_pix = neuromancer.MultinoulliNLL(probs, target, target_is_sparse=True) loss = neuromancer.AggregateLoss(loss_pix, name='loss') errors = neuromancer.Errors(probs, target, target_is_sparse=True) model = neuromancer.model_manager.getmodel() model.designate_nodes( input_node=inp, target_node=target, loss_node=loss, prediction_node=probs, prediction_ext=[loss, errors, probs] ) return model
def create_model(): from elektronn2 import neuromancer as nm in_sh = (None, 1, 26, 26) inp = nm.Input(in_sh, 'b,f,y,x', name='raw') out = nm.Conv(inp, 12, (3, 3), (2, 2), batch_normalisation='train') out = nm.Conv(out, 36, (3, 3), (2, 2), batch_normalisation='train') out = nm.Conv(out, 64, (3, 3), (1, 1), batch_normalisation='train') out = nm.Perceptron(out, 200, flatten=True) out = nm.Perceptron(out, 10, activation_func='lin') out = nm.Softmax(out) target = nm.Input_like(out, override_f=1, name='target') loss = nm.MultinoulliNLL(out, target, name='nll_', target_is_sparse=True) # Objective loss = nm.AggregateLoss(loss) # Monitoring / Debug outputs errors = nm.Errors(out, target, target_is_sparse=True) model = nm.model_manager.getmodel() model.designate_nodes(input_node=inp, target_node=target, loss_node=loss, prediction_node=out, prediction_ext=[loss, errors, out]) return model
def create_model(): from elektronn2 import neuromancer act = 'relu' in_sh = (batch_size, 1 if data_init_kwargs["raw_only"] else 4, nb_views, int(x), int(y)) inp = neuromancer.Input(in_sh, 'b,f,z,y,x', name='raw') out0 = neuromancer.Conv(inp, 13, (1, 5, 5), (1, 2, 2), activation_func=act, dropout_rate=dr) out0 = neuromancer.Conv(out0, 19, (1, 5, 5), (1, 2, 2), activation_func=act, dropout_rate=dr) out0 = neuromancer.Conv(out0, 25, (1, 4, 4), (1, 2, 2), activation_func=act, dropout_rate=dr) out0 = neuromancer.Conv(out0, 25, (1, 4, 4), (1, 2, 2), activation_func=act, dropout_rate=dr) out0 = neuromancer.Conv(out0, 30, (1, 2, 2), (1, 2, 2), activation_func=act, dropout_rate=dr) out0 = neuromancer.Conv(out0, 30, (1, 1, 1), (1, 2, 2), activation_func=act, dropout_rate=dr) out = neuromancer.Conv(out0, 31, (1, 1, 1), (1, 1, 1), activation_func=act, dropout_rate=dr) out = neuromancer.Perceptron(out, 50, flatten=True, dropout_rate=dr) out = neuromancer.Perceptron(out, 30, flatten=True, dropout_rate=dr) out = neuromancer.Perceptron(out, 4, activation_func='lin') out = neuromancer.Softmax(out) target = neuromancer.Input_like(out, override_f=1, name='target') weights = neuromancer.ValueNode((4, ), 'f', value=(1, 1, 2, 2)) loss = neuromancer.MultinoulliNLL(out, target, name='nll_', target_is_sparse=True, class_weights=weights) # Objective loss = neuromancer.AggregateLoss(loss) # Monitoring / Debug outputs errors = neuromancer.Errors(out, target, target_is_sparse=True) model = neuromancer.model_manager.getmodel() model.designate_nodes(input_node=inp, target_node=target, loss_node=loss, prediction_node=out, prediction_ext=[loss, errors, out]) return model
def create_model(): from elektronn2 import neuromancer in_sh = (20, 20 * 58) inp = neuromancer.Input(in_sh, 'b,f', name='raw') out = neuromancer.Perceptron(inp, 700, 'lin') out = neuromancer.Perceptron(inp, 500, 'lin') out = neuromancer.Perceptron(inp, 300, 'lin') out = neuromancer.Perceptron(out, 2*58, 'lin') out = neuromancer.Softmax(out, n_indep=58) target = neuromancer.Input_like(out, override_f=1, name='target') weights = neuromancer.ValueNode((116, ), 'f', value=[0.2, 1.8]*58) loss = neuromancer.MultinoulliNLL( out, target, target_is_sparse=True, class_weights=weights, name='nll' ) # Objective loss = neuromancer.AggregateLoss(loss) # Monitoring / Debug outputs errors = neuromancer.Errors(out, target, target_is_sparse=True) model = neuromancer.model_manager.getmodel() model.designate_nodes( input_node=inp, target_node=target, loss_node=loss, prediction_node=out, prediction_ext=[loss, errors, out] ) return model
def create_model(): from elektronn2 import neuromancer as nm in_sh = (None, 1, 22, 140, 140) inp = nm.Input(in_sh, 'b,f,z,x,y', name='raw') # high res # Convolution, downsampling of intermediate features conv0 = nm.Conv(inp, 20, (1, 3, 3)) conv1 = nm.Conv(conv0, 20, (1, 3, 3)) down0 = nm.Pool(conv1, (1, 2, 2), mode='max') # mid res conv2 = nm.Conv(down0, 30, (1, 3, 3)) conv3 = nm.Conv(conv2, 30, (1, 3, 3)) down1 = nm.Pool(conv3, (1, 2, 2), mode='max') # low res conv4 = nm.Conv(down1, 35, (1, 3, 3)) conv5 = nm.Conv(conv4, 35, (1, 3, 3)) down2 = nm.Pool(conv5, (1, 2, 2), mode='max') # very low res conv6 = nm.Conv(down2, 42, (3, 3, 3)) down2b = nm.Pool(conv6, (1, 2, 2), mode='max') # very low res, even lower conv7 = nm.Conv(down2b, 42, (3, 3, 3)) # Merging very low-res features with low-res features mrg0 = nm.UpConvMerge(conv5, conv7, 45) mconv0 = nm.Conv(mrg0, 42, (1, 3, 3)) mconv1 = nm.Conv(mconv0, 42, (1, 3, 3)) # Merging low-res with mid-res features mrg1 = nm.UpConvMerge(conv3, mconv1, 42) mconv2 = nm.Conv(mrg1, 35, (3, 3, 3)) mconv3 = nm.Conv(mconv2, 35, (3, 3, 3)) # Merging mid-res with high-res features mrg2 = nm.UpConvMerge(conv1, mconv3, 30) mconv4 = nm.Conv(mrg2, 20, (3, 3, 3)) mconv5 = nm.Conv(mconv4, 20, (3, 3, 3)) barr = nm.Conv(mconv5, 2, (1, 1, 1), activation_func='lin', name='barr') probs = nm.Softmax(barr) target = nm.Input_like(mconv5, override_f=1, name='target') loss_pix = nm.MultinoulliNLL(probs, target, target_is_sparse=True, name='nll_barr') loss = nm.AggregateLoss(loss_pix, name='loss') errors = nm.Errors(probs, target, target_is_sparse=True) model = nm.model_manager.getmodel() model.designate_nodes(input_node=inp, target_node=target, loss_node=loss, prediction_node=probs, prediction_ext=[loss, errors, probs]) return model
def create_model(): from elektronn2 import neuromancer in_sh = (40, 40, 58) inp = neuromancer.Input(in_sh, 'r,b,f', name='raw') inp0, _ = neuromancer.split(inp, 'r', index=1, strip_singleton_dims=True) inp_mem = neuromancer.InitialState_like(inp0, override_f=750, init_kwargs={ 'mode': 'fix-uni', 'scale': 0.1 }) out = neuromancer.GRU(inp0, inp_mem, 750) out = neuromancer.Scan(out, inp_mem, in_iterate=inp, in_iterate_0=inp0, n_steps=40, last_only=True) out = neuromancer.Perceptron(out, 2 * 58, 'lin') out = neuromancer.Softmax(out, n_indep=58) target = neuromancer.Input_like(out, override_f=1, name='target') weights = neuromancer.ValueNode((116, ), 'f', value=(0.2, 1.8)) loss = neuromancer.MultinoulliNLL(out, target, name='nll', target_is_sparse=True, class_weights=weights) # Objective loss = neuromancer.AggregateLoss(loss) # Monitoring / Debug outputs errors = neuromancer.Errors(out, target, target_is_sparse=True) model = neuromancer.model_manager.getmodel() model.designate_nodes(input_node=inp, target_node=target, loss_node=loss, prediction_node=out, prediction_ext=[loss, errors, out]) return model
def create_model(): from elektronn2 import neuromancer import theano.tensor as T import numpy as np in_sh = (None,1,572-32*9,572-32*9) img = neuromancer.Input(in_sh, 'b,f,x,y', name='raw') out0 = neuromancer.Conv(img, 64, (3,3), (1,1)) out1 = neuromancer.Conv(out0, 64, (3,3), (1,1)) out2 = neuromancer.Pool(out1, (2,2)) out3 = neuromancer.Conv(out2, 128, (3,3), (1,1)) out4 = neuromancer.Conv(out3, 128, (3,3), (1,1)) out5 = neuromancer.Pool(out4, (2,2)) out6 = neuromancer.Conv(out5, 256, (3,3), (1,1)) out7 = neuromancer.Conv(out6, 256, (3,3), (1,1)) out8 = neuromancer.Pool(out7, (2,2)) out9 = neuromancer.Conv(out8, 512, (3,3), (1,1)) out10 = neuromancer.Conv(out9, 512, (3,3), (1,1)) out11 = neuromancer.Pool(out10, (2,2)) out12 = neuromancer.Conv(out11, 1024, (3,3), (1,1)) out13 = neuromancer.Conv(out12, 1024, (3,3), (1,1)) out14 = neuromancer.Pool(out13, (2,2)) #### up0 = neuromancer.UpConvMerge(out10, out14, 1024) up1 = neuromancer.Conv(up0, 512, (3,3), (1,1)) up2 = neuromancer.Conv(up1, 512, (3,3), (1,1)) up3 = neuromancer.UpConvMerge(out7, up2, 512) up4 = neuromancer.Conv(up3, 256, (3,3), (1,1)) up5 = neuromancer.Conv(up4, 256, (3,3), (1,1)) up6 = neuromancer.UpConvMerge(out4, up5, 256) up7 = neuromancer.Conv(up6, 128, (3,3), (1,1)) up8 = neuromancer.Conv(up7, 128, (3,3), (1,1)) up9 = neuromancer.UpConvMerge(out1, up8, 128) up10 = neuromancer.Conv(up9, 64, (3,3), (1,1)) top_feat = neuromancer.Conv(up10, 64, (3,3), (1,1)) # Target outputs barr_out = neuromancer.Conv(top_feat, 3, (1,1), (1,1), activation_func='lin', name='barr') obj_out = neuromancer.Conv(top_feat, 4, (1,1), (1,1), activation_func='lin', name='obj') my_out = neuromancer.Conv(top_feat, 3, (1,1), (1,1), activation_func='lin', name='my') barr_out = neuromancer.Softmax(barr_out) obj_out = neuromancer.Softmax(obj_out) my_out = neuromancer.Softmax(my_out) target = neuromancer.Input_like(top_feat, dtype='int16', override_f=3, name='target') barr, obj, my = neuromancer.split(target, 'f', n_out=3, name=['barr_t', 'obj_t', 'my_t']) # Target loss barr_loss_pix = neuromancer.MultinoulliNLL(barr_out, barr, target_is_sparse=True,name='nll_barr') obj_loss_pix = neuromancer.MultinoulliNLL(obj_out, obj, target_is_sparse=True, name='nll_obj') my_loss_pix = neuromancer.MultinoulliNLL(my_out, my, target_is_sparse=True, name='nll_my') pred = neuromancer.Concat([barr_out, obj_out, my_out], axis='f') pred.feature_names = ['barrier_bg', 'barr_mem', 'barr_ecs', 'obj_bg', 'obj_mito', 'obj_ves', 'obj_syn', 'my_bg', 'my_out', 'my_in'] # Objective weights = np.array([2.154, 0.42, 0.42]) weights *= len(weights) / weights.sum() loss = neuromancer.AggregateLoss([barr_loss_pix, obj_loss_pix, my_loss_pix], mixing_weights=weights) # Monitoring / Debug outputs nll_barr = neuromancer.ApplyFunc(barr_loss_pix, T.mean, name='mnll_barr') nll_obj = neuromancer.ApplyFunc(obj_loss_pix, T.mean, name='mnll_obj') nll_my = neuromancer.ApplyFunc(my_loss_pix, T.mean, name='mnll_my') errors = neuromancer.Errors(barr_out, barr, target_is_sparse=True) model = neuromancer.model_manager.getmodel() model.designate_nodes(input_node=img, target_node=target, loss_node=loss, prediction_node=pred, prediction_ext=[loss, errors, pred], debug_outputs =[nll_barr, errors, nll_obj, nll_my]) return model
def demo_new_gm(): mfp = False in_sh = (1, 1, 15, 198, 198) if mfp else (1, 1, 25, 171, 171) inp = neuromancer.Input(in_sh, 'b,f,z,x,y', name='raw') out = neuromancer.Conv(inp, 20, (1, 6, 6), (1, 1, 1), mfp=mfp, batch_normalisation='train') out = neuromancer.Conv(out, 40, (1, 5, 5), (1, 2, 2), mfp=mfp) out = neuromancer.Conv(out, 50, (1, 4, 4), (1, 2, 2), mfp=mfp) out = neuromancer.Conv(out, 80, (1, 4, 4), (1, 1, 1), mfp=mfp) out = neuromancer.Conv(out, 80, (4, 1, 1), (2, 1, 1), mfp=mfp) # first z kernel, 2 pool out = neuromancer.Conv(out, 80, (3, 4, 4), (1, 1, 1), mfp=mfp) out = neuromancer.Conv(out, 80, (3, 4, 4), (1, 1, 1), mfp=mfp) out = neuromancer.Conv(out, 100, (2, 4, 4), (1, 1, 1), mfp=mfp) out = neuromancer.Conv(out, 120, (2, 4, 4), (1, 1, 1), mfp=mfp) out = neuromancer.Conv(out, 120, (1, 2, 2), (1, 1, 1), mfp=mfp) out = neuromancer.Conv(out, 120, (1, 1, 1), (1, 1, 1), mfp=mfp) out1, out2 = neuromancer.split(out, 1, n_out=2) probs = neuromancer.Conv(out1, 2, (1, 1, 1), (1, 1, 1), mfp=mfp, activation_func='lin') probs = neuromancer.Softmax(probs, name='probs') discard, mode = neuromancer.split(probs, 1, n_out=2) concentration = neuromancer.Conv(out2, 1, (1, 1, 1), (1, 1, 1), mfp=mfp, activation_func='lin', name='concentration') t_sh = probs.shape.copy() t_sh.updateshape('f', 1) target = neuromancer.Input_like(t_sh, dtype='float32', name='target') loss_pix = neuromancer.BetaNLL(mode, concentration, target) loss = neuromancer.AggregateLoss(loss_pix) errors = neuromancer.Errors(probs, target, target_is_sparse=True) prediction = neuromancer.Concat([mode, concentration], axis=1, name='prediction') loss_std = neuromancer.ApplyFunc(loss_pix, T.std) model = neuromancer.model_manager.getmodel() model.designate_nodes(input_node=inp, target_node=target, loss_node=loss, prediction_node=prediction, prediction_ext=[loss, errors, prediction]) ### --- ### model2 = neuromancer.model_manager.newmodel("second") inp2 = neuromancer.Input(in_sh, 'b,f,z,x,y', name='raw') out2 = neuromancer.Conv(inp2, 20, (1, 6, 6), (1, 1, 1), mfp=mfp) out2 = neuromancer.Conv(out2, 40, (1, 5, 5), (1, 2, 2), mfp=mfp) out2 = neuromancer.Conv(out2, 50, (1, 4, 4), (1, 2, 2), mfp=mfp) out2 = neuromancer.Conv(out2, 120, (2, 4, 4), (1, 1, 1), mfp=mfp) out2 = neuromancer.Conv(out2, 120, (1, 2, 2), (1, 1, 1), mfp=mfp) out2 = neuromancer.Conv(out2, 120, (1, 1, 1), (1, 1, 1), mfp=mfp) probs2 = neuromancer.Conv(out2, 2, (1, 1, 1), (1, 1, 1), mfp=mfp, activation_func='lin') probs2 = neuromancer.Softmax(probs2, name='probs') t_sh = probs2.shape.copy() t_sh.updateshape('f', 1) target2 = neuromancer.Input_like(t_sh, dtype='float32', name='target') loss_pix2 = neuromancer.MultinoulliNLL(probs2, target2) loss2 = neuromancer.AggregateLoss(loss_pix2) errors2 = neuromancer.Errors(probs2, target2, target_is_sparse=True) model2.designate_nodes(input_node=inp2, target_node=target2, loss_node=loss2, prediction_node=probs2, prediction_ext=[loss2, errors2, probs2]) model.save('/tmp/test.pkl') model2.save('/tmp/test2.pkl') model2_reloaded = modelload('/tmp/test2.pkl') model2_reloaded.save('/tmp/test2_reloaded.pkl') print(neuromancer.model_manager)