def test_set_optimizer(self): n_data_points = 20 n_features = 2 X = np.random.rand(n_data_points, n_features) y = [[0, 1] for x in range(n_data_points)] dataset = NumpyDataset(X, y) features = Feature(shape=(None, n_features)) dense = Dense(out_channels=2, in_layers=[features]) output = SoftMax(in_layers=[dense]) label = Label(shape=(None, 2)) smce = SoftMaxCrossEntropy(in_layers=[label, dense]) loss = ReduceMean(in_layers=[smce]) tg = dc.models.TensorGraph(learning_rate=0.01, use_queue=False) tg.add_output(output) tg.set_loss(loss) global_step = tg.get_global_step() learning_rate = ExponentialDecay(initial_rate=0.1, decay_rate=0.96, decay_steps=100000) tg.set_optimizer(GradientDescent(learning_rate=learning_rate)) tg.fit(dataset, nb_epoch=1000) prediction = np.squeeze(tg.predict_on_batch(X)) tg.save() tg1 = TensorGraph.load_from_dir(tg.model_dir) prediction2 = np.squeeze(tg1.predict_on_batch(X)) assert_true(np.all(np.isclose(prediction, prediction2, atol=0.01)))
def test_save_load(self): n_data_points = 20 n_features = 2 X = np.random.rand(n_data_points, n_features) y = [[0, 1] for x in range(n_data_points)] dataset = NumpyDataset(X, y) features = Feature(shape=(None, n_features)) dense = Dense(out_channels=2, in_layers=[features]) output = SoftMax(in_layers=[dense]) label = Label(shape=(None, 2)) smce = SoftMaxCrossEntropy(in_layers=[label, dense]) loss = ReduceMean(in_layers=[smce]) tg = dc.models.TensorGraph(learning_rate=0.01) tg.add_output(output) tg.set_loss(loss) submodel_loss = ReduceSum(in_layers=smce) submodel_opt = Adam(learning_rate=0.002) submodel = tg.create_submodel(layers=[dense], loss=submodel_loss, optimizer=submodel_opt) tg.fit(dataset, nb_epoch=1) prediction = np.squeeze(tg.predict_on_batch(X)) tg.save() dirpath = tempfile.mkdtemp() shutil.rmtree(dirpath) shutil.move(tg.model_dir, dirpath) tg1 = TensorGraph.load_from_dir(dirpath) prediction2 = np.squeeze(tg1.predict_on_batch(X)) assert_true(np.all(np.isclose(prediction, prediction2, atol=0.01)))
def test_save_load(self): n_data_points = 20 n_features = 2 X = np.random.rand(n_data_points, n_features) y = [[0, 1] for x in range(n_data_points)] dataset = NumpyDataset(X, y) features = Feature(shape=(None, n_features)) dense = Dense(out_channels=2, in_layers=[features]) output = SoftMax(in_layers=[dense]) label = Label(shape=(None, 2)) smce = SoftMaxCrossEntropy(in_layers=[label, dense]) loss = ReduceMean(in_layers=[smce]) tg = dc.models.TensorGraph(learning_rate=0.01) tg.add_output(output) tg.set_loss(loss) submodel_loss = ReduceSum(in_layers=smce) submodel_opt = Adam(learning_rate=0.002) submodel = tg.create_submodel( layers=[dense], loss=submodel_loss, optimizer=submodel_opt) tg.fit(dataset, nb_epoch=1) prediction = np.squeeze(tg.predict_on_batch(X)) tg.save() dirpath = tempfile.mkdtemp() shutil.rmtree(dirpath) shutil.move(tg.model_dir, dirpath) tg1 = TensorGraph.load_from_dir(dirpath) prediction2 = np.squeeze(tg1.predict_on_batch(X)) assert_true(np.all(np.isclose(prediction, prediction2, atol=0.01)))
def test_set_optimizer(self): n_data_points = 20 n_features = 2 X = np.random.rand(n_data_points, n_features) y = [[0, 1] for x in range(n_data_points)] dataset = NumpyDataset(X, y) features = Feature(shape=(None, n_features)) dense = Dense(out_channels=2, in_layers=[features]) output = SoftMax(in_layers=[dense]) label = Label(shape=(None, 2)) smce = SoftMaxCrossEntropy(in_layers=[label, dense]) loss = ReduceMean(in_layers=[smce]) tg = dc.models.TensorGraph(learning_rate=0.01, use_queue=False) tg.add_output(output) tg.set_loss(loss) global_step = tg.get_global_step() learning_rate = ExponentialDecay( initial_rate=0.1, decay_rate=0.96, decay_steps=100000) tg.set_optimizer(GradientDescent(learning_rate=learning_rate)) tg.fit(dataset, nb_epoch=1000) prediction = np.squeeze(tg.predict_on_batch(X)) tg.save() tg1 = TensorGraph.load_from_dir(tg.model_dir) prediction2 = np.squeeze(tg1.predict_on_batch(X)) assert_true(np.all(np.isclose(prediction, prediction2, atol=0.01)))
def test_graph_save(self): n_samples = 10 n_features = 11 n_tasks = 1 batch_size = 10 X = np.random.rand(batch_size, n_samples, n_features) y = np.ones(shape=(n_samples, n_tasks)) ids = np.arange(n_samples) dataset = dc.data.NumpyDataset(X, y, None, ids) g = TensorGraph(model_dir='/tmp/tmpss5_ki5_') inLayer = Input(shape=(None, n_samples, n_features)) g.add_feature(inLayer) flatten = Flatten() g.add_layer(flatten, parents=[inLayer]) dense = Dense(out_channels=1) g.add_layer(dense, parents=[flatten]) g.add_output(dense) label_out = Input(shape=(None, 1)) g.add_label(label_out) loss = LossLayer() g.add_layer(loss, parents=[dense, label_out]) g.set_loss(loss) g.fit(dataset, nb_epoch=100) g.save() g1 = TensorGraph.load_from_dir('/tmp/tmpss5_ki5_') print(g1) print(g1.predict_on_batch(X))
def test_graph_save(self): n_samples = 10 n_features = 11 n_tasks = 1 batch_size = 10 X = np.random.rand(batch_size, n_samples, n_features) y = np.ones(shape=(n_samples, n_tasks)) ids = np.arange(n_samples) dataset = dc.data.NumpyDataset(X, y, None, ids) g = TensorGraph(model_dir='/tmp/tmpss5_ki5_') inLayer = Input(shape=(None, n_samples, n_features)) g.add_feature(inLayer) flatten = Flatten() g.add_layer(flatten, parents=[inLayer]) dense = Dense(out_channels=1) g.add_layer(dense, parents=[flatten]) g.add_output(dense) label_out = Input(shape=(None, 1)) g.add_label(label_out) loss = LossLayer() g.add_layer(loss, parents=[dense, label_out]) g.set_loss(loss) g.fit(dataset, nb_epoch=100) g.save() g1 = TensorGraph.load_from_dir('/tmp/tmpss5_ki5_') print(g1) print(g1.predict_on_batch(X))
def test_save_load(self): n_data_points = 20 n_features = 2 X = np.random.rand(n_data_points, n_features) y = [[0, 1] for x in range(n_data_points)] dataset = NumpyDataset(X, y) features = Feature(shape=(None, n_features)) dense = Dense(out_channels=2, in_layers=[features]) output = SoftMax(in_layers=[dense]) label = Label(shape=(None, 2)) smce = SoftMaxCrossEntropy(in_layers=[label, dense]) loss = ReduceMean(in_layers=[smce]) tg = dc.models.TensorGraph(learning_rate=0.01) tg.add_output(output) tg.set_loss(loss) tg.fit(dataset, nb_epoch=1) prediction = np.squeeze(tg.predict_on_batch(X)) tg.save() tg1 = TensorGraph.load_from_dir(tg.model_dir) prediction2 = np.squeeze(tg1.predict_on_batch(X)) assert_true(np.all(np.isclose(prediction, prediction2, atol=0.01)))