def test(self, teX, teY, task, batch_size=100):
vround = np.vectorize(lambda x: int(round(
x))) # vround turns outputs from probabilities to binary 0/1
if self.mode == "frozen" or self.mode == "unfrozen":
cnn = self.nets[task]
probabilities = np.asarray([])
for start, end in zip(
range(0, len(teX), batch_size),
range(batch_size,
len(teX) + batch_size, batch_size)):
probabilities = np.append(
probabilities,
cnn.predict_probs(teX[start:end])[:, 1])
predictions = vround(probabilities)
elif self.mode == "stacking":
predictions = []
for cnn in self.nets[task]:
probabilities = np.asarray([])
for start, end in zip(
range(0, len(teX), batch_size),
range(batch_size,
len(teX) + batch_size, batch_size)):
probabilities = np.append(
probabilities,
cnn.predict_probs(teX[start:end])[:, 1])
predictions.append(probabilities)
# combine predictions from each of the task's nets
predictions = vround(np.mean(predictions, axis=0))
return np.mean(binarize(teY, task)[:, np.newaxis] == predictions)
def train(self, trX, trY, epochs=10, verbose=False, batch_size=100):
if self.mode == "frozen":
# find any new tasks that we don't already have a net for
tasks = np.setdiff1d(np.unique(trY), np.asarray(self.tasks))
elif self.mode == "unfrozen" or self.mode == "stacking":
# use all tasks
tasks = np.unique(trY)
# for each one, train it on a binarized random sampling, keeping all positive examples of
# the current task and using a percentage of all other tasks as the negative examples,
# since we need both positive and negative examples to properly train a neural network
for task in tasks:
if verbose:
print("Training new net for task {0}".format(task))
trXr, trYr = random_sampling(data_set=trX,
data_labels=trY,
p_kept=0.2,
to_keep=task)
trB = binarize(trYr, task)[:, np.newaxis]
trB = np.concatenate((np.logical_not(trB).astype(np.int64), trB),
axis=1)
if self.mode == "frozen" or self.mode == "unfrozen":
prev = None if len(self.nets) == 0 else self.nets[self.newest]
elif self.mode == "stacking":
prev = None if len(
self.nets) == 0 else self.nets[self.newest][-1]
if self.mode == "unfrozen" and task in np.asarray(self.tasks):
cnn = self.nets[task]
cnn.train_net(epochs, batch_size, trX=trXr, trY=trB)
else:
cnn = self.nnet(trXr, trB, prev, epochs)
self.tasks.append(task)
self.newest = task
if self.mode == "frozen" or self.mode == "unfrozen":
self.nets[task] = cnn
elif self.mode == "stacking":
if task not in self.nets:
self.nets[task] = []
self.nets[task].append(cnn)
return self
def test(self, teX, teY, task, batch_size = 100): vround = np.vectorize(lambda x: int(round(x))) # vround turns outputs from probabilities to binary 0/1 if self.mode == "frozen" or self.mode == "unfrozen": cnn = self.nets[task] probabilities = np.asarray([]) for start, end in zip(range(0, len(teX), batch_size), range(batch_size, len(teX)+batch_size, batch_size)): probabilities = np.append(probabilities, cnn.predict_probs(teX[start:end])[:, 1]) predictions = vround(probabilities) elif self.mode == "stacking": predictions = [] for cnn in self.nets[task]: probabilities = np.asarray([]) for start, end in zip(range(0, len(teX), batch_size), range(batch_size, len(teX)+batch_size, batch_size)): probabilities = np.append(probabilities, cnn.predict_probs(teX[start:end])[:, 1]) predictions.append(probabilities) # combine predictions from each of the task's nets predictions = vround(np.mean(predictions, axis = 0)) return np.mean(binarize(teY, task)[:, np.newaxis] == predictions)
def train(self, trX, trY, epochs = 10, verbose = False, batch_size = 100):
if self.mode == "frozen":
# find any new tasks that we don't already have a net for
tasks = np.setdiff1d(np.unique(trY), np.asarray(self.tasks))
elif self.mode == "unfrozen" or self.mode == "stacking":
# use all tasks
tasks = np.unique(trY)
# for each one, train it on a binarized random sampling, keeping all positive examples of
# the current task and using a percentage of all other tasks as the negative examples,
# since we need both positive and negative examples to properly train a neural network
for task in tasks:
if verbose:
print("Training new net for task {0}".format(task))
trXr, trYr = random_sampling(data_set = trX, data_labels = trY, p_kept = 0.2, to_keep = task)
trB = binarize(trYr, task)[:, np.newaxis]
trB = np.concatenate((np.logical_not(trB).astype(np.int64), trB), axis = 1)
if self.mode == "frozen" or self.mode == "unfrozen":
prev = None if len(self.nets) == 0 else self.nets[self.newest]
elif self.mode == "stacking":
prev = None if len(self.nets) == 0 else self.nets[self.newest][-1]
if self.mode == "unfrozen" and task in np.asarray(self.tasks):
cnn = self.nets[task]
cnn.train_net(epochs, batch_size, trX = trXr, trY = trB)
else:
cnn = self.nnet(trXr, trB, prev, epochs)
self.tasks.append(task)
self.newest = task
if self.mode == "frozen" or self.mode == "unfrozen":
self.nets[task] = cnn
elif self.mode == "stacking":
if task not in self.nets:
self.nets[task] = []
self.nets[task].append(cnn)
return self
if __name__ == "__main__":
caffe.set_device(0)
caffe.set_mode_gpu()
net = caffe.Net("examples/mnist/lenet_auto_test.prototxt",
"examples/mnist/lenet_iter_10000.caffemodel", caffe.TEST)
trX, trY = open_dataset("examples/mnist/mnist_train_lmdb")
teX, teY = open_dataset("examples/mnist/mnist_test_lmdb")
predictions = net.predict(teX)
print("\nNet Predictions:")
print("Predicted: {0}".format(Counter(predictions)))
print("Actual: {0}".format(Counter(teY)))
print("Accuracy: {0:0.04f}".format(np.mean(predictions == teY)))
# todo:
# figure out how to extract activations for top-layer model
# these seem to be the weights, not activations
print("\nNet Blobs:")
for key, val in net.blobs.items():
print(" {0}, {1}".format(key, val.data.shape))
# make binary nets for multi-net model
# I can change values here, but it needs to be done before training and loading the nets
print("\nBinarized Labels:")
print(trY[:20].tolist())
for c in range(10):
print(binarize(trY[:20], c).tolist())
]
teA = np.concatenate(teAs)
print("teA.shape: {0}".format(teA.shape))
trX, teX, trY, teY = load.mnist(onehot=True)
trC = np.argmax(trY, axis=1)
print("trC.shape: {0}".format(trC.shape))
teC = np.argmax(teY, axis=1)
print("teC.shape: {0}".format(teC.shape))
print("Done.")
print("\nCreating ELLA Model...")
num_params = 625
num_latent = 20
ella = ELLA.ELLA(num_params, num_latent, LogisticRegression, mu=10**-3)
for task in range(10):
result_vector = binarize(trC, task)
ella.fit(trA, result_vector, task)
print("Trained task {0}".format(task))
print("Sparsity coefficients: {0}".format(ella.S))
print("Done.")
print("\nAnalyzing Training Data...")
predictions = np.argmax(np.asarray(
[ella.predict_logprobs(trA, i) for i in range(ella.T)]),
axis=0)
print("predictions.shape: {0}".format(predictions.shape))
accuracy = np.mean(predictions == trC)
print("accuracy: {0:0.04f}".format(accuracy))
print("per-task binary accuracy:")
for task_id in range(ella.T):
print(" {0} - {1:0.04f}".format(
def calculate_catastrophic_interference(num_tasks, exclude_start, exclude_end, top_layer = "cnn", save_figs = False, verbose = False, epochs = 20, batch_size = 100):
excluded = range(exclude_start, exclude_end)
task_nums = [i for i in range(num_tasks) if i not in excluded]
start = time.time()
cnn = ConvolutionalNeuralNetwork()
cnn.initialize_mnist()
# cnn.trX = cnn.trX[:int(len(cnn.trX)*.2)]
# cnn.trY = cnn.trY[:int(len(cnn.trY)*.2)]
# cnn.teX = cnn.teX[:int(len(cnn.teX)*.2)]
# cnn.teY = cnn.teY[:int(len(cnn.teY)*.2)]
cnn.trX, cnn.trY, trXE, trYE = split_dataset(excluded, cnn.trX, cnn.trY)
cnn.teX, cnn.teY, teXE, teYE = split_dataset(excluded, cnn.teX, cnn.teY)
cnn.create_model_functions()
colors = ["#00FF00", "#0000FF", "#00FFFF", "#FFFF00", "#FF00FF", "#000000", "#888888", "#FF8800", "#88FF00", "#FF0088"]
print("\nTraining on tasks {0}, excluding tasks {1}".format(task_nums, excluded))
base_accuracies = train_per_task(cnn, num_tasks, verbose, epochs, batch_size)
end = time.time()
print("Initial training: {0:0.02f}sec".format(end-start))
# base model, trained without excluded tasks
# (which are then added back in one of the three top-layer models)
# if save_figs:
# for t in task_nums:
# plt.plot(np.arange(0, epochs), accuracies[t], color = colors[t])
# plt.plot(np.arange(0, epochs), accuracies["total"], color = "#FF0000", marker = "o")
# plt.axis([0, epochs-1, 0, 1])
# plt.xlabel("Epoch")
# plt.ylabel("Accuracy")
# plt.title("Model Accuracy")
# plt.legend(["Task {0}".format(t) for t in task_nums]+["Total"], loc = "lower right")
# plt.savefig("figures/trained on {0}, excluded {1}.png".format(task_nums, excluded), bbox_inches = "tight")
# plt.close()
total_trX = np.concatenate((cnn.trX, trXE), axis = 0)
total_trY = np.concatenate((cnn.trY, trYE), axis = 0)
total_teX = np.concatenate((cnn.teX, teXE), axis = 0)
total_teY = np.concatenate((cnn.teY, teYE), axis = 0)
num_chunks = 20
trA = np.concatenate([cnn.activate(total_trX[(len(total_trX)/num_chunks*i):(len(total_trX)/num_chunks*(i+1))]) for i in range(num_chunks)])
teA = cnn.activate(total_teX)
trC = np.argmax(total_trY, axis = 1)
teC = np.argmax(total_teY, axis = 1)
# convolutional neural network
if "cnn" in top_layer:
print("\nRetraining convolutional neural network on all tasks after excluding {0} from initial training".format(excluded))
start = time.time()
# fit model with data
cnn_accs = train_new_tasks(cnn, total_trX, total_trY, total_teX, total_teY, num_tasks, verbose, epochs, batch_size)
end = time.time()
print("ConvNet Retraining: {0:0.02f}sec".format(end-start))
# show accuracy improvement from additional model layer
print("[ConvNet(exclusion)] Testing data accuracy: {0:0.04f}".format(base_accuracies["total"][-1]))
print("[ConvNet(exclusion)+ConvNet(all)] Testing data accuracy: {0:0.04f}".format(cnn_accs["total"][-1]))
print("[(CN(E)+CN(A))-CN(E)] Accuracy improvement: {0:0.04f}".format(cnn_accs["total"][-1]-base_accuracies["total"][-1]))
# generate and save accuracy figures
if save_figs:
for t in range(num_tasks):
plt.plot(np.arange(0, epochs), cnn_accs[t], color = colors[t])
plt.plot(np.arange(0, epochs), cnn_accs["total"], color = "#FF0000", marker = "o")
plt.legend(["Task {0}".format(t) for t in task_nums]+["Total"], loc = "lower right")
plt.axis([0, epochs-1, 0, 1])
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Model Accuracy")
plt.savefig("figures/trained on {0}, excluded {1}, then retrained on all.png".format(task_nums, excluded), bbox_inches = "tight")
plt.close()
# efficient lifelong learning algorithm
if "ella" in top_layer:
print("\nTraining efficient lifelong learning algorithm on all tasks after excluding {0} from convnet training".format(excluded))
start = time.time()
# fit model with data
ella = ELLA(d = 625, k = 5, base_learner = LogisticRegression, base_learner_kwargs = {"tol": 10**-2}, mu = 10**-3)
for task in range(num_tasks):
ella.fit(trA, binarize(trC, task), task)
predictions = np.argmax(np.asarray([ella.predict_logprobs(teA, i) for i in range(ella.T)]), axis = 0)
ella_acc = np.mean(predictions == teC)
end = time.time()
print("ELLA: {0:0.02f}sec".format(end-start))
# show accuracy improvement from additional model layer
print("[ConvNet] Testing data accuracy: {0:0.04f}".format(base_accuracies["total"][-1]))
print("[ConvNet+ELLA] Testing data accuracy: {0:0.04f}".format(ella_acc))
print("[(CN+ELLA)-CN] Accuracy improvement: {0:0.04f}".format(ella_acc-base_accuracies["total"][-1]))
# generate and save accuracy figures
if save_figs:
pass # need to generate per-task or per-epoch accuracies to have a good visualization
# logistic regression model
if "lr" in top_layer:
print("\nTraining logistic regression model on all tasks after excluding {0} from convnet training".format(excluded))
start = time.time()
# fit model with data
lr = LogisticRegression()
lr.fit(trA, trC)
logreg_accs = find_model_task_accuracies(lr, num_tasks, teA, teC)
end = time.time()
print("Logistic Regression: {0:0.02f}sec".format(end-start))
# show accuracy improvement from additional model layer
print("[ConvNet] Testing data accuracy: {0:0.04f}".format(base_accuracies["total"][-1]))
print("[ConvNet+LogReg] Testing data accuracy: {0:0.04f}".format(logreg_accs["total"]))
print("[(CN+LR)-CN] Accuracy improvement: {0:0.04f}".format(logreg_accs["total"]-base_accuracies["total"][-1]))
if verbose:
print("\nLogistic regression model accuracies after exclusion training:")
for key, value in logreg_accs.items():
print("Task: {0}, accuracy: {1:0.04f}".format(key, value))
# generate and save accuracy figures
if save_figs:
plotX = ["Task {0}".format(t) for t in range(num_tasks)]+["Total", "Average"]
plotY = [logreg_accs[t] for t in range(num_tasks)]+[logreg_accs["total"], np.mean(logreg_accs.values())]
plt.bar(range(len(plotX)), plotY)
plt.xticks(range(len(plotX)), plotX)
plt.title("Model Accuracy")
plt.savefig("figures/trained on {0}, excluded {1}, then logreg.png".format(task_nums, excluded), bbox_inches = "tight")
plt.close()
# support vector classifier
if "svc" in top_layer:
print("\nTraining linear support vector classifier on all tasks after excluding {0} from convnet training".format(excluded))
start = time.time()
# fit model with data
svc = LinearSVC()
svc.fit(trA, trC)
svc_accs = find_model_task_accuracies(svc, num_tasks, teA, teC)
end = time.time()
print("Support Vector Classifier: {0:0.02f}sec".format(end-start))
# show accuracy improvement from additional model layer
print("[ConvNet] Testing data accuracy: {0:0.04f}".format(base_accuracies["total"][-1]))
print("[ConvNet+SVC] Testing data accuracy: {0:0.04f}".format(svc_accs["total"]))
print("[(CN+SVC)-CN] Accuracy improvement: {0:0.04f}".format(svc_accs["total"]-base_accuracies["total"][-1]))
if verbose:
print("\nSupport vector classifier accuracies after exclusion training:")
for key, value in svc_accs.items():
print("Task: {0}, accuracy: {1:0.04f}".format(key, value))
# generate and save accuracy figures
if save_figs:
plotX = ["Task {0}".format(t) for t in range(num_tasks)]+["Total", "Average"]
plotY = [svc_accs[t] for t in range(num_tasks)]+[svc_accs["total"], np.mean(svc_accs.values())]
plt.bar(range(len(plotX)), plotY)
plt.xticks(range(len(plotX)), plotX)
plt.title("Model Accuracy")
plt.savefig("figures/trained on {0}, excluded {1}, then svc.png".format(task_nums, excluded), bbox_inches = "tight")
plt.close()
print("")
def calculate_catastrophic_interference(num_tasks,
exclude_start,
exclude_end,
top_layer="cnn",
save_figs=False,
verbose=False,
epochs=20,
batch_size=100):
excluded = range(exclude_start, exclude_end)
task_nums = [i for i in range(num_tasks) if i not in excluded]
start = time.time()
cnn = ConvolutionalNeuralNetwork()
cnn.initialize_mnist()
# cnn.trX = cnn.trX[:int(len(cnn.trX)*.2)]
# cnn.trY = cnn.trY[:int(len(cnn.trY)*.2)]
# cnn.teX = cnn.teX[:int(len(cnn.teX)*.2)]
# cnn.teY = cnn.teY[:int(len(cnn.teY)*.2)]
cnn.trX, cnn.trY, trXE, trYE = split_dataset(excluded, cnn.trX, cnn.trY)
cnn.teX, cnn.teY, teXE, teYE = split_dataset(excluded, cnn.teX, cnn.teY)
cnn.create_model_functions()
colors = [
"#00FF00", "#0000FF", "#00FFFF", "#FFFF00", "#FF00FF", "#000000",
"#888888", "#FF8800", "#88FF00", "#FF0088"
]
print("\nTraining on tasks {0}, excluding tasks {1}".format(
task_nums, excluded))
base_accuracies = train_per_task(cnn, num_tasks, verbose, epochs,
batch_size)
end = time.time()
print("Initial training: {0:0.02f}sec".format(end - start))
# base model, trained without excluded tasks
# (which are then added back in one of the three top-layer models)
# if save_figs:
# for t in task_nums:
# plt.plot(np.arange(0, epochs), accuracies[t], color = colors[t])
# plt.plot(np.arange(0, epochs), accuracies["total"], color = "#FF0000", marker = "o")
# plt.axis([0, epochs-1, 0, 1])
# plt.xlabel("Epoch")
# plt.ylabel("Accuracy")
# plt.title("Model Accuracy")
# plt.legend(["Task {0}".format(t) for t in task_nums]+["Total"], loc = "lower right")
# plt.savefig("figures/trained on {0}, excluded {1}.png".format(task_nums, excluded), bbox_inches = "tight")
# plt.close()
total_trX = np.concatenate((cnn.trX, trXE), axis=0)
total_trY = np.concatenate((cnn.trY, trYE), axis=0)
total_teX = np.concatenate((cnn.teX, teXE), axis=0)
total_teY = np.concatenate((cnn.teY, teYE), axis=0)
num_chunks = 20
trA = np.concatenate([
cnn.activate(total_trX[(len(total_trX) / num_chunks *
i):(len(total_trX) / num_chunks * (i + 1))])
for i in range(num_chunks)
])
teA = cnn.activate(total_teX)
trC = np.argmax(total_trY, axis=1)
teC = np.argmax(total_teY, axis=1)
# convolutional neural network
if "cnn" in top_layer:
print(
"\nRetraining convolutional neural network on all tasks after excluding {0} from initial training"
.format(excluded))
start = time.time()
# fit model with data
cnn_accs = train_new_tasks(cnn, total_trX, total_trY, total_teX,
total_teY, num_tasks, verbose, epochs,
batch_size)
end = time.time()
print("ConvNet Retraining: {0:0.02f}sec".format(end - start))
# show accuracy improvement from additional model layer
print(
"[ConvNet(exclusion)] Testing data accuracy: {0:0.04f}"
.format(base_accuracies["total"][-1]))
print(
"[ConvNet(exclusion)+ConvNet(all)] Testing data accuracy: {0:0.04f}"
.format(cnn_accs["total"][-1]))
print(
"[(CN(E)+CN(A))-CN(E)] Accuracy improvement: {0:0.04f}"
.format(cnn_accs["total"][-1] - base_accuracies["total"][-1]))
# generate and save accuracy figures
if save_figs:
for t in range(num_tasks):
plt.plot(np.arange(0, epochs), cnn_accs[t], color=colors[t])
plt.plot(np.arange(0, epochs),
cnn_accs["total"],
color="#FF0000",
marker="o")
plt.legend(["Task {0}".format(t) for t in task_nums] + ["Total"],
loc="lower right")
plt.axis([0, epochs - 1, 0, 1])
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Model Accuracy")
plt.savefig(
"figures/trained on {0}, excluded {1}, then retrained on all.png"
.format(task_nums, excluded),
bbox_inches="tight")
plt.close()
# efficient lifelong learning algorithm
if "ella" in top_layer:
print(
"\nTraining efficient lifelong learning algorithm on all tasks after excluding {0} from convnet training"
.format(excluded))
start = time.time()
# fit model with data
ella = ELLA(d=625,
k=5,
base_learner=LogisticRegression,
base_learner_kwargs={"tol": 10**-2},
mu=10**-3)
for task in range(num_tasks):
ella.fit(trA, binarize(trC, task), task)
predictions = np.argmax(np.asarray(
[ella.predict_logprobs(teA, i) for i in range(ella.T)]),
axis=0)
ella_acc = np.mean(predictions == teC)
end = time.time()
print("ELLA: {0:0.02f}sec".format(end - start))
# show accuracy improvement from additional model layer
print(
"[ConvNet] Testing data accuracy: {0:0.04f}"
.format(base_accuracies["total"][-1]))
print(
"[ConvNet+ELLA] Testing data accuracy: {0:0.04f}"
.format(ella_acc))
print(
"[(CN+ELLA)-CN] Accuracy improvement: {0:0.04f}"
.format(ella_acc - base_accuracies["total"][-1]))
# generate and save accuracy figures
if save_figs:
pass # need to generate per-task or per-epoch accuracies to have a good visualization
# logistic regression model
if "lr" in top_layer:
print(
"\nTraining logistic regression model on all tasks after excluding {0} from convnet training"
.format(excluded))
start = time.time()
# fit model with data
lr = LogisticRegression()
lr.fit(trA, trC)
logreg_accs = find_model_task_accuracies(lr, num_tasks, teA, teC)
end = time.time()
print("Logistic Regression: {0:0.02f}sec".format(end - start))
# show accuracy improvement from additional model layer
print(
"[ConvNet] Testing data accuracy: {0:0.04f}"
.format(base_accuracies["total"][-1]))
print(
"[ConvNet+LogReg] Testing data accuracy: {0:0.04f}"
.format(logreg_accs["total"]))
print(
"[(CN+LR)-CN] Accuracy improvement: {0:0.04f}"
.format(logreg_accs["total"] - base_accuracies["total"][-1]))
if verbose:
print(
"\nLogistic regression model accuracies after exclusion training:"
)
for key, value in logreg_accs.items():
print("Task: {0}, accuracy: {1:0.04f}".format(key, value))
# generate and save accuracy figures
if save_figs:
plotX = ["Task {0}".format(t)
for t in range(num_tasks)] + ["Total", "Average"]
plotY = [logreg_accs[t] for t in range(num_tasks)
] + [logreg_accs["total"],
np.mean(logreg_accs.values())]
plt.bar(range(len(plotX)), plotY)
plt.xticks(range(len(plotX)), plotX)
plt.title("Model Accuracy")
plt.savefig(
"figures/trained on {0}, excluded {1}, then logreg.png".format(
task_nums, excluded),
bbox_inches="tight")
plt.close()
# support vector classifier
if "svc" in top_layer:
print(
"\nTraining linear support vector classifier on all tasks after excluding {0} from convnet training"
.format(excluded))
start = time.time()
# fit model with data
svc = LinearSVC()
svc.fit(trA, trC)
svc_accs = find_model_task_accuracies(svc, num_tasks, teA, teC)
end = time.time()
print("Support Vector Classifier: {0:0.02f}sec".format(end - start))
# show accuracy improvement from additional model layer
print(
"[ConvNet] Testing data accuracy: {0:0.04f}"
.format(base_accuracies["total"][-1]))
print(
"[ConvNet+SVC] Testing data accuracy: {0:0.04f}"
.format(svc_accs["total"]))
print(
"[(CN+SVC)-CN] Accuracy improvement: {0:0.04f}"
.format(svc_accs["total"] - base_accuracies["total"][-1]))
if verbose:
print(
"\nSupport vector classifier accuracies after exclusion training:"
)
for key, value in svc_accs.items():
print("Task: {0}, accuracy: {1:0.04f}".format(key, value))
# generate and save accuracy figures
if save_figs:
plotX = ["Task {0}".format(t)
for t in range(num_tasks)] + ["Total", "Average"]
plotY = [svc_accs[t] for t in range(num_tasks)
] + [svc_accs["total"],
np.mean(svc_accs.values())]
plt.bar(range(len(plotX)), plotY)
plt.xticks(range(len(plotX)), plotX)
plt.title("Model Accuracy")
plt.savefig(
"figures/trained on {0}, excluded {1}, then svc.png".format(
task_nums, excluded),
bbox_inches="tight")
plt.close()
print("")
teAs = [np.asarray(load_activations("saved/teA{0:02d}.txt".format(i), (10000 / num_chunks, 625)).eval()) for i in range(num_chunks)]
teA = np.concatenate(teAs)
print("teA.shape: {0}".format(teA.shape))
trX, teX, trY, teY = load.mnist(onehot = True)
trC = np.argmax(trY, axis = 1)
print("trC.shape: {0}".format(trC.shape))
teC = np.argmax(teY, axis = 1)
print("teC.shape: {0}".format(teC.shape))
print("Done.")
print("\nCreating ELLA Model...")
num_params = 625
num_latent = 20
ella = ELLA.ELLA(num_params, num_latent, LogisticRegression, mu = 10 ** -3)
for task in range(10):
result_vector = binarize(trC, task)
ella.fit(trA, result_vector, task)
print("Trained task {0}".format(task))
print("Sparsity coefficients: {0}".format(ella.S))
print("Done.")
print("\nAnalyzing Training Data...")
predictions = np.argmax(np.asarray([ella.predict_logprobs(trA, i) for i in range(ella.T)]), axis = 0)
print("predictions.shape: {0}".format(predictions.shape))
accuracy = np.mean(predictions == trC)
print("accuracy: {0:0.04f}".format(accuracy))
print("per-task binary accuracy:")
for task_id in range(ella.T):
print(" {0} - {1:0.04f}".format(task_id, np.mean(binarize(predictions, task_id) == binarize(trC, task_id))))
print("Done.")