def fit(self,
x_train,
y_train,
batch_size=32,
epochs=100,
validation_data=None,
class_weight=None):
history = self.model.fit(x=x_train,
y=y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=self.callback_list,
validation_data=validation_data)
utils.plot_metrics(history.history)
def main(epochs, load_data, opt_adam, lr, weight_decay, read_from_saved,
read_from_file, force_cpu, nosave):
if force_cpu:
device = torch.device("cpu")
else:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.float
print("Running on:", device)
if read_from_file is not None:
network = SNN.from_file(read_from_file)
elif read_from_saved:
network = SNN.from_file('logs/save_network.net',
device=device,
dtype=dtype)
else:
network = SNN.from_last_checkpoint(device=device, dtype=dtype)
network.time_expector = time_expector
# network.notifier = notify # FIXME
if opt_adam:
opt = RAdam(network.get_trainable_parameters(lr, weight_decay))
else:
opt = torch.optim.SGD(network.get_trainable_parameters(
lr, weight_decay),
lr=lr,
momentum=0.9)
if not nosave:
with open('logs/results.log', 'a+') as f:
res_metrics = network.fit(load_data,
epochs=epochs,
optimizer=opt,
result_file=f,
save_checkpoints=True)
else:
res_metrics = network.fit(load_data,
epochs=epochs,
optimizer=opt,
save_checkpoints=False)
plot_metrics(res_metrics,
save_plot_path=None if nosave else './logs/metrics_C_')
if not nosave:
network.save('logs/save_network.net')
def run_modelpat(train_generatorp, test_generatorp, val_generatorp):
adam = Adam(lr=3e-5)
#randomly shuffling training data
#X_train , y_train = shuffle(X_train , y_train)
scores = []
print("current running model is model pat ")
model = make_unet(img_shape)
#model.load_weights('/tmp/weights25th_logging'+str(i)+'.hdf5')
model.compile(optimizer=adam,
loss=soft_dice_loss,
metrics=[dice_coeff, 'acc', sp, sn])
model.summary()
save_model_path = 'Other/unetartery.hdf5'
earlystopper = EarlyStopping(patience=15, verbose=1)
cp = ModelCheckpoint(filepath=save_model_path,
monitor='val_loss',
save_best_only=True,
verbose=1)
history = model.fit_generator(
train_generatorp,
steps_per_epoch=90,
epochs=200,
validation_data=val_generatorp,
validation_steps=25,
callbacks=[cp,
earlystopper]) # , #earlystopper])#,tensorboard_callback])
scores.append(
model.evaluate_generator(test_generatorp,
steps=12,
workers=1,
verbose=1))
save_all(model, 25)
plot_metrics(history, 25)
print("finished model ")
#model.reset_states()
return scores, model
def train_and_test_model(model, save_dir, data_loader, learning_rate=0.01,
epochs=2, gpu=False, plot=False):
# initialize weights and biases
for layer in iter(model.classifier.children()):
if len(layer.state_dict()) > 0:
layer.bias.data.fill_(0)
layer.weight.data.normal_(std=0.01)
# freeze vgg's parameters so that we can initialize the last layer's weights
for param in model.parameters():
param.requires_grad = False
# enable training the classifier with the new output
for param in model.classifier[-1].parameters():
param.requires_grad_()
best_acc_epoch = 0
best_acc = 0.0
avg_loss_val = 0
avg_acc_val = 0
train_losses = []
train_accuracy = []
valid_losses = []
valid_accuracy = []
best_model_wts = copy.deepcopy(model.state_dict())
print("TRAINING MODEL with following parameters")
print("\tsave_dir (for checkpoints):", save_dir)
print("\tlearning_rate:", learning_rate)
print("\tepochs:", epochs)
print("\tgpu:", gpu)
if gpu and not torch.cuda.is_available():
print("Sorry, no cuda enabled gpu is available.")
print("Training on the cpu...")
gpu = False
if gpu:
model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model.classifier[-1].parameters(), lr=0.01, momentum=0.9)
since = time.time()
for e in range(epochs):
for phase in ['train', 'valid']:
correct = 0
running_loss = 0
for inputs,labels in data_loader[phase]:
batch_loss, batch_correct = run_batch(phase, gpu, inputs, labels,
optimizer_ft, model, criterion)
running_loss += batch_loss # loss.item()
# for computing accuracy
correct += batch_correct # torch.sum(preds == labels.data)
# free gpu resources
torch.cuda.empty_cache()
epoch_acc = float(correct) / float(dataset_sizes[phase])
if phase == "train":
train_losses.append(running_loss)
train_accuracy.append(epoch_acc)
else: # "valid"
valid_losses.append(running_loss)
valid_accuracy.append(epoch_acc)
if epoch_acc > best_acc:
best_acc = epoch_acc
best_acc_epoch = e
best_model_wts = copy.deepcopy(model.state_dict())
print("Epoch {}: {}\n\tTotal Loss: {:4f}\n\tAccuracy: {:d}/{:d} = {:4f}\n".format(
e, phase.upper(), running_loss, correct, dataset_sizes[phase], epoch_acc))
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print("Best Accuracy: {} in epoch {}".format(round(best_acc, 3), best_acc_epoch))
if plot:
utils.plot_metrics(train_losses, valid_losses, train_accuracy, valid_accuracy, e)
# Test data
phase = 'test'
correct = 0
for inputs,labels in data_loader[phase]:
batch_loss, batch_correct = run_batch(phase, gpu, inputs, labels,
optimizer_ft, model, criterion)
running_loss += batch_loss
# for computing accuracy
correct += batch_correct
# free gpu resources
torch.cuda.empty_cache()
test_acc = float(correct) / float(dataset_sizes[phase])
print("{}\nTotal Loss: {:4f}\nAccuracy: {:d}/{:d} = {:4f}".format(phase.upper(),
running_loss, correct, dataset_sizes[phase], test_acc))
return model, optimizer_ft, e
def train(self,
X_train,
Y_train,
X_val,
Y_val,
max_epochs,
batch_size,
learning_rate_init,
reg_param=0,
learning_rate_decay_type='inverse',
learning_rate_decay_parameter=10,
early_stopping=True,
save_path='./UNet',
reset_parameters=False,
check_val_every_n_batches=5,
seed=0,
data_on_GPU=True):
'''
Trains the network on the given data, provided as numpy arrays. It is assumed all preprocessing has already been done, including shuffling and splitting of the data into training/validation sets.
'''
# (Re)load base graph from file
print('Loading graph...')
saver = self.load_graph(save_path)
with self.G.as_default():
print('Inserting data augmentation operations...')
# Load data onto GPU, replace the input placeholder with an index into the data on the GPU (if applicable)
m_train, height, width, n_channels = X_train.shape
m_val = X_val.shape[0]
m = m_train + m_val
n_classes = Y_train.shape[-1]
if data_on_GPU:
X_train_t = tf.constant(X_train, dtype=tf.uint8)
X_val_t = tf.constant(X_val, dtype=tf.uint8)
Y_train_t = tf.constant(Y_train, dtype=tf.bool)
Y_val_t = tf.constant(Y_val, dtype=tf.bool)
del X_train, X_val, Y_train, Y_val
train_idx = tf.placeholder_with_default([0], shape=[None])
X_train_t = tf.gather(X_train_t, train_idx, axis=0)
Y_train_t = tf.gather(Y_train_t, train_idx, axis=0)
else:
X_train_t = tf.placeholder_with_default(np.zeros(
[0, height, width, n_channels], dtype=np.uint8),
shape=self.input.shape,
name='X_train_input')
X_val_t = tf.placeholder_with_default(np.zeros(
[0, height, width, n_channels], dtype=np.uint8),
shape=self.input.shape,
name='X_val_input')
Y_train_t = tf.placeholder_with_default(
np.zeros([0, height, width, n_classes], dtype=np.bool),
shape=self.labels.shape,
name='Y_train_input')
Y_val_t = tf.placeholder_with_default(np.zeros(
[0, height, width, n_classes], dtype=np.bool),
shape=self.labels.shape,
name='Y_val_input')
# Insert data augmentation steps to graph
train_or_val_idx = tf.placeholder(dtype=tf.int32, shape=[None])
X_train_aug, Y_train_aug = self.data_augmentation(
X_train_t, Y_train_t)
X = tf.cast(
tf.gather(tf.concat([X_train_aug, X_val_t], axis=0),
train_or_val_idx), tf.float32)
Y = tf.cast(
tf.gather(tf.concat([Y_train_aug, Y_val_t], axis=0),
train_or_val_idx), tf.float32)
ge.swap_ts([X, Y],
[self.input, self.labels]) # Use X and Y from now on!
# Write to log file
with open(str(save_path) + '.log', 'w+') as fo:
fo.write('Training log\n\n')
fo.write('Dataset metrics:\n')
fo.write('Training data shape: {}\n'.format(X_train.shape))
fo.write('Validation set size: {}\n'.format(m_val))
# fo.write('X_mean: {}\n'.format(X_mean))
# fo.write('X_std: {}\n\n'.format(X_std))
fo.write('Hyperparameters:\n')
fo.write('Batch size: {}\n'.format(batch_size))
fo.write('Learning rate: {}\n'.format(learning_rate_init))
fo.write('Learning rate annealed every N epochs: {}\n'.format(
learning_rate_decay_parameter))
fo.write('Learning rate anneal type: {}\n'.format(
learning_rate_decay_type))
# fo.write('Stepped anneal: {}\n'.format(STEPPED_ANNEAL))
# fo.write('Regularization type: {}\n'.format(REGULARIZATION_TYPE))
fo.write('Regularization parameter: {}\n'.format(reg_param))
# fo.write('Input noise variance: {:.2f}\n'.format(INPUT_NOISE_MAGNITUDE**2))
# fo.write('Weight noise variance: {:.2f}\n'.format(WEIGHT_NOISE_MAGNITUDE**2))
# for n in range(1,len(KEEP_PROB)+1):
# fo.write('Dropout keep prob. group {}: {:.2f}\n'.format(n, KEEP_PROB[n]))
fo.write('Logging frequency: {} global steps\n'.format(
check_val_every_n_batches))
fo.write('Random seed: {}\n'.format(seed))
# Initialize control flow variables and logs
# max_val_accuracy = -1
# max_val_conf = -1
best_val_loss = np.inf
global_step = 0
# with open(str(save_path)+'_val_accuracy.log', 'w+') as fo:
# fo.write('')
with open(str(save_path) + '_val_loss.log', 'w+') as fo:
fo.write('')
# with open(str(save_path)+'_val_confidence.log', 'w+') as fo:
# fo.write('')
# with open(str(save_path)+'_train_accuracy.log', 'w+') as fo:
# fo.write('')
with open(str(save_path) + '_train_loss.log', 'w+') as fo:
fo.write('')
# with open(str(save_path)+'_train_confidence.log', 'w+') as fo:
# fo.write('')
with open(str(save_path) + '_learning_rate.log', 'w+') as fo:
fo.write('')
# Start tensorflow session, reset_parameters or reload checkpoint
print('Starting tensorflow session...')
with tf.Session() as sess:
if reset_parameters:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
else:
saver.restore(sess, save_path)
uninitialized_vars = []
for var in tf.global_variables():
try:
sess.run(var)
except tf.errors.FailedPreconditionError:
uninitialized_vars.append(var)
sess.run(tf.variables_initializer(uninitialized_vars))
# Iterate over training epochs
best_val_loss = np.inf
for epoch in range(max_epochs):
if learning_rate_decay_type == 'inverse':
learning_rate = learning_rate_init / (
1 + epoch / learning_rate_decay_parameter)
elif learning_rate_decay_type == 'constant':
learning_rate = learning_rate_init
elif learning_rate_decay_type == 'exponential':
learning_rate = learning_rate_init * np.exp(
-epoch / learning_rate_decay_parameter)
else:
raise Exception('Unknown learning rate decay function')
# Iterate over batches:
n_batches = math.ceil(m_train / batch_size)
for b in range(n_batches):
train_idx_i = b * batch_size
train_idx_f = min((b + 1) * batch_size, m_train)
if data_on_GPU:
feed_dict = {
train_idx: range(train_idx_i, train_idx_f + 1),
train_or_val_idx:
range(train_idx_f - train_idx_i),
self.learning_rate: learning_rate,
self.reg_param: reg_param
}
else:
feed_dict = {
X_train_t: X_train[train_idx_i:train_idx_f],
Y_train_t: Y_train[train_idx_i:train_idx_f],
train_or_val_idx:
range(train_idx_f - train_idx_i),
self.learning_rate: learning_rate,
self.reg_param: reg_param
}
train_loss, _ = sess.run([self.loss, self.train_op],
feed_dict=feed_dict)
print('Epoch {}, batch {}/{}: loss={:.3e}'.format(
epoch + 1, b, n_batches, train_loss))
if np.isnan(train_loss) or np.isinf(train_loss):
print('Detected nan, exiting training')
quit()
exit()
break
if (global_step % check_val_every_n_batches) == 0:
if data_on_GPU:
feed_dict = {
train_or_val_idx: range(1, m_val + 1),
self.reg_param: reg_param
}
else:
feed_dict = {
X_val_t: X_val,
Y_val_t: Y_val,
train_or_val_idx: range(m_val),
self.reg_param: reg_param
}
val_loss = sess.run(self.loss, feed_dict=feed_dict)
if early_stopping and (val_loss < best_val_loss):
best_val_loss = val_loss
print(
'New best validation loss: {:.3e}! Saving...'
.format(val_loss))
saver.save(sess,
save_path,
write_meta_graph=False)
# Write to logs everytime validation set run
with open(str(save_path) + '_train_loss.log',
'a') as fo:
fo.write(str(train_loss) + '\n')
# with open(str(save_path)+'_train_accuracy.log', 'a') as fo:
# fo.write(str(train_accuracy)+'\n')
# with open(str(save_path)+'_train_confidence.log', 'a') as fo:
# fo.write(str(train_conf)+'\n')
# with open(str(save_path)+'_val_accuracy.log', 'a') as fo:
# fo.write(str(val_accuracy)+'\n')
with open(str(save_path) + '_val_loss.log',
'a') as fo:
fo.write(str(val_loss) + '\n')
# with open(str(save_path)+'_val_confidence.log', 'a') as fo:
# fo.write(str(val_conf)+'\n')
with open(
str(save_path) + '_learning_rate.log',
'a') as fo:
fo.write(str(learning_rate) + '\n')
u.plot_metrics(str(save_path))
global_step += 1
logger.add_scalar(epoch, 'train.loss', train_loss)
logger.add_scalar(epoch, 'val.loss', val_loss)
logger.add_scalar(epoch, 'train.acc', train_acc)
logger.add_scalar(epoch, 'val.acc', val_acc)
if args.odenet:
logger.add_scalar(epoch, 'train.forward_nfe',
train_forward_nfe.avg)
logger.add_scalar(epoch, 'train.backward_nfe',
train_backward_nfe.avg)
logger.add_scalar(epoch, 'val.forward_nfe', val_nfe)
logger.add_scalar(epoch, 'time', time.time() - t0)
t0 = time.time()
logger.iter_info()
logger.save()
if epoch % args.vis_each == 0 or epoch == 1 or epoch == args.epochs:
utils.plot_metrics(logger.scalar_metrics, args.odenet)
plt.savefig(os.path.join(args.root, 'training_curves.png'))
plt.close()
torch.save(model.state_dict(), os.path.join(args.root, 'model_final.torch'))
torch.save(optimizer.state_dict(),
os.path.join(args.root, 'optimizer_final.torch'))
if args.verbose:
print(model)
for param in model.named_parameters():
print(param)
parser.done()
def main():
st.title("Binary Classification Web App")
st.sidebar.title("Binary Classification Web App")
st.markdown("Are your mushrooms edible or poisonous? 🍄")
st.sidebar.markdown("Are your mushrooms edible or poisonous? 🍄")
df = utils.load_data()
x_train, x_test, y_train, y_test = utils.split(df)
class_names = ["edible", "poisonous"]
st.sidebar.subheader("Choose Classifier")
classifier = st.sidebar.selectbox(
"Classifier", ("Support Vector Machine (SVM)", "Logistic Regression",
"Random Forest Classification"))
if classifier == 'Support Vector Machine (SVM)':
st.sidebar.subheader("Model Hyperparameters")
C = st.sidebar.number_input("C (Regularization parameter)",
0.01,
10.0,
step=0.01,
key='C')
kernel = st.sidebar.radio("Kernel", ("rbf", "linear"), key='kernel')
gamma = st.sidebar.radio("Gamma (Kernel Coefficient)",
("scale", "auto"),
key='gamma')
metrics = st.sidebar.multiselect(
"What matrix to plot?",
("Confusion Matrix", "ROC Curve", "Precision-Recall Curve"))
if st.sidebar.button("Classify", key="classify"):
st.subheader("Support Vector Machine (SVM) Results")
model = SVC(C=C, kernel=kernel, gamma=gamma)
model.fit(x_train, y_train)
accuracy = model.score(x_test, y_test)
y_pred = model.predict(x_test)
st.write("Accuracy: ", accuracy.round(2))
st.write(
"Precision: ",
precision_score(y_test, y_pred, labels=class_names).round(2))
st.write("Recall: ",
recall_score(y_test, y_pred, labels=class_names).round(2))
utils.plot_metrics(metrics, model, x_test, y_test, class_names)
if classifier == 'Logistic Regression':
st.sidebar.subheader("Model Hyperparameters")
C = st.sidebar.number_input("C (Regularization parameter)",
0.01,
10.0,
step=0.01,
key='Lr')
max_iter = st.sidebar.slider("Maximum no. of iterations",
100,
500,
key='max_iter')
metrics = st.sidebar.multiselect(
"What matrix to plot?",
("Confusion Matrix", "ROC Curve", "Precision-Recall Curve"))
if st.sidebar.button("Classify", key="classify"):
st.subheader("Logistic Regression Results")
model = LogisticRegression(C=C, max_iter=max_iter)
model.fit(x_train, y_train)
accuracy = model.score(x_test, y_test)
y_pred = model.predict(x_test)
st.write("Accuracy: ", accuracy.round(2))
st.write(
"Precision: ",
precision_score(y_test, y_pred, labels=class_names).round(2))
st.write("Recall: ",
recall_score(y_test, y_pred, labels=class_names).round(2))
utils.plot_metrics(metrics, model, x_test, y_test, class_names)
if classifier == 'Random Forest Classification':
st.sidebar.subheader("Model Hyperparameters")
n_estimators = st.sidebar.number_input(
"This is the number of trees in the forest",
100,
5000,
step=10,
key='n_estimators')
max_depth = st.sidebar.number_input("The maximum depth of the tree",
1,
100,
step=2,
key='max_depth')
bootstrap = st.sidebar.radio("Bootstrap samples when building trees",
("True", "False"),
key='bootstrap')
metrics = st.sidebar.multiselect(
"What matrix to plot?",
("Confusion Matrix", "ROC Curve", "Precision-Recall Curve"))
if st.sidebar.button("Classify", key="classify"):
st.subheader("Random Forest Results")
model = RandomForestClassifier(n_estimators=n_estimators,
max_depth=max_depth,
bootstrap=bootstrap,
n_jobs=-1)
model.fit(x_train, y_train)
accuracy = model.score(x_test, y_test)
y_pred = model.predict(x_test)
st.write("Accuracy: ", accuracy.round(2))
st.write(
"Precision: ",
precision_score(y_test, y_pred, labels=class_names).round(2))
st.write("Recall: ",
recall_score(y_test, y_pred, labels=class_names).round(2))
utils.plot_metrics(metrics, model, x_test, y_test, class_names)
if st.sidebar.checkbox("Show raw data", False):
st.subheader("Mushroom Data Set (Classification)")
st.write(df)
eps_history.append(agent_dqn.epsilon)
avg_score = np.mean(
score_history[-100:]) # moving average over last 100 games
print('game-episode: ', i, 'episode-score: ', score,
' || average-score %.2f' % avg_score,
'max-score %.2f' % max_score,
' || epsilon %.2f' % agent_dqn.epsilon, 'num_steps',
num_steps)
if score > max_score:
max_score = score # Possible improvemen - short moving max_score average
agent_dqn.save_network_checkpoints()
print("Training done!")
plot_metrics(steps_arr, score_history, eps_history, figure_file_name)
# Capturing the agent playing
if create_capture:
img_arr = []
agent_dqn.epsilon = 0.0001
observation = env.reset()
for i in range(1000):
frame = np.uint8(observation[0] * 255)
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
img_arr.append(frame)
action = agent_dqn.choose_action(observation)
observation, _, _, _ = env.step(action)
setproctitle("(hwijeen) word drop")
logging.basicConfig(
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
if __name__ == "__main__":
DATA_DIR = '/home/nlpgpu5/hwijeen/MulDocSumm/data/'
FILE = 'rottentomatoes_prepared'
DEVICE = torch.device('cuda:1')
EPOCH = 50
WORD_DROP = 0.2
data = MulSumData(DATA_DIR, FILE, 99, DEVICE)
selfattnCVAE = build_SelfAttnCVAE(len(data.vocab),
hidden_dim=600,
latent_dim=300,
enc_bidirectional=True,
word_drop=WORD_DROP,
device=DEVICE)
trainer = Trainer(selfattnCVAE,
data,
lr=0.001,
to_record=['recon_loss', 'kl_loss'])
results = trainer.train(num_epoch=EPOCH, verbose=True)
plot_learning_curve(results['train_losses'], results['valid_losses'])
plot_metrics(results['train_metrics'], results['valid_metrics'])
plot_kl_loss(trainer.stats.stats['kl_loss'])
def main(config):
# For Reproducibility #
random.seed(config.seed)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(config.seed)
# Weights and Plots Path #
paths = [config.weights_path, config.plots_path]
for path in paths:
make_dirs(path)
# Prepare Data Loader #
if config.dataset == 'cifar':
train_loader, val_loader, test_loader = cifar_loader(
config.num_classes, config.batch_size)
input_size = 32
# Prepare Networks #
if config.model == 'vit':
model = VisionTransformer(in_channels=config.in_channels,
embed_dim=config.embed_dim,
patch_size=config.patch_size,
num_layers=config.num_layers,
num_heads=config.num_heads,
mlp_dim=config.mlp_dim,
dropout=config.drop_out,
input_size=input_size,
num_classes=config.num_classes).to(device)
elif config.model == 'efficient':
model = EfficientNet.from_name(
'efficientnet-b0', num_classes=config.num_classes).to(device)
elif config.model == 'resnet':
model = resnet34(pretrained=False).to(device)
model.fc = nn.Linear(config.mlp_dim, config.num_classes).to(device)
else:
raise NotImplementedError
# Weight Initialization #
if not config.model == 'efficient':
if config.init == 'normal':
model.apply(init_weights_normal)
elif config.init == 'xavier':
model.apply(init_weights_xavier)
elif config.init == 'he':
model.apply(init_weights_kaiming)
else:
raise NotImplementedError
# Train #
if config.phase == 'train':
# Loss Function #
criterion = nn.CrossEntropyLoss()
# Optimizers #
if config.num_classes == 10:
optimizer = torch.optim.Adam(model.parameters(),
lr=config.lr,
betas=(0.5, 0.999))
optimizer_scheduler = get_lr_scheduler(config.lr_scheduler,
optimizer)
elif config.num_classes == 100:
optimizer = torch.optim.SGD(model.parameters(),
lr=config.lr,
momentum=0.9,
weight_decay=5e-4)
optimizer_scheduler = get_lr_scheduler('step', optimizer)
# Constants #
best_top1_acc = 0
# Lists #
train_losses, val_losses = list(), list()
train_top1_accs, train_top5_accs = list(), list()
val_top1_accs, val_top5_accs = list(), list()
# Train and Validation #
print("Training {} has started.".format(model.__class__.__name__))
for epoch in range(config.num_epochs):
# Train #
train_loss, train_top1_acc, train_top5_acc = train(
train_loader, model, optimizer, criterion, epoch, config)
# Validation #
val_loss, val_top1_acc, val_top5_acc = validate(
val_loader, model, criterion, epoch, config)
# Add items to Lists #
train_losses.append(train_loss)
val_losses.append(val_loss)
train_top1_accs.append(train_top1_acc)
train_top5_accs.append(train_top5_acc)
val_top1_accs.append(val_top1_acc)
val_top5_accs.append(val_top5_acc)
# If Best Top 1 Accuracy #
if val_top1_acc > best_top1_acc:
best_top1_acc = max(val_top1_acc, best_top1_acc)
# Save Models #
print("The best model is saved!")
torch.save(
model.state_dict(),
os.path.join(
config.weights_path,
'BEST_{}_{}_{}.pkl'.format(model.__class__.__name__,
str(config.dataset).upper(),
config.num_classes)))
print("Best Top 1 Accuracy {:.2f}%\n".format(best_top1_acc))
# Optimizer Scheduler #
optimizer_scheduler.step()
# Plot Losses and Accuracies #
losses = (train_losses, val_losses)
accs = (train_top1_accs, train_top5_accs, val_top1_accs, val_top5_accs)
plot_metrics(losses, accs, config.plots_path, model, config.dataset,
config.num_classes)
print("Training {} using {} {} finished.".format(
model.__class__.__name__,
str(config.dataset).upper(), config.num_classes))
# Test #
elif config.phase == 'test':
test(test_loader, model, config)
else:
raise NotImplementedError
def train(G, max_episodes, save_path):
'''
Trains a DQN to play pong. Periodically saves progress to a checkpoint file, and saves plots of several metrics to monitor training.
Input:
G: computational graph by which the action-value function Q is calculated.
max_episodes: the maximum number of episodes to run for before terminating training
save_path: a file path to the location of the checkpoint files
Output: none
'''
# Define some constants, lists, metrics, etc
action_map = {1: 'x', 2: '^', 3: 'v'} # Stay, up, down
replay_memory = u.ReplayMemory(max_exp_len=REPLAY_MEM_LEN)
step_list = []
reward_list = []
avg_reward = None
val_Q_list = []
episode_length_list = []
episode_time_list = []
avg_episode_length_list = []
avg_episode_length = None
episode_score_list = {'player': [], 'computer': []}
X_val = u.load_validation_screens()
# Initialize the Pong gym environment, set seeds
env = gym.make('Pong-v0')
np.random.seed(SEED)
tf.set_random_seed(SEED)
plt.ioff()
# Gather screens
# Initialize computational graph
with G.as_default():
# Get input/output tensors
X = G.get_tensor_by_name('X:0')
Y = G.get_tensor_by_name('Y:0')
Q = G.get_tensor_by_name('Q:0')
A = G.get_tensor_by_name('A:0')
L = G.get_tensor_by_name('L:0')
LR = G.get_tensor_by_name('LR:0')
train_op = G.get_operation_by_name('TrainOp')
saver = tf.train.Saver()
# Initialize TF session
with tf.Session() as sess:
# Reload/initialize variables
if RELOAD_PARAMETERS:
print('Reloading from last checkpoint...')
saver.restore(sess, save_path)
else:
print('Initializing variables...')
sess.run(tf.global_variables_initializer())
# Iterate over episodes
global_steps = 0
for episode in range(max_episodes):
tic = time.time()
obs = u.preprocess_image(env.reset())
for i in range(3):
replay_memory.add_frame(
np.zeros((160 // DOWNSAMPLE, 160 // DOWNSAMPLE),
dtype=bool))
replay_memory.add_frame(obs)
# Iterate over frames
done = False
frame = 0
episode_score = [0, 0]
while not done:
if (global_steps >= OBSERVE_STEPS):
# Feed state into DQN
s = np.stack(
[replay_memory.frames[i] for i in range(-4, 0)],
axis=-1).reshape(1, 160 // DOWNSAMPLE,
160 // DOWNSAMPLE, 4)
y = sess.run(Y, feed_dict={X: s})
# Decide on action
epsilon = max(
MAX_EPSILON *
(1 - global_steps / EPSILON_ANNEALING_STEPS),
MIN_EPSILON)
if (np.random.rand() < epsilon):
a = np.random.choice([1, 2, 3])
else:
a = np.argmax(y) + 1
else:
a = np.random.choice([1, 2, 3])
# Take action, observe environment, reward
obs, r, done, _ = env.step(a)
r_sum = r
for i in range(STEPS_TO_SKIP):
obs, r, done_temp, _ = env.step(1)
r_sum += r
if done_temp == True:
done = True
if r_sum > 0:
episode_score[0] += int(r_sum)
elif r_sum < 0:
episode_score[1] -= int(r_sum)
# Add new state/reward to replay memory
replay_memory.add_frame(u.preprocess_image(obs))
experience = (np.stack(list(replay_memory.frames),
axis=-1).astype(bool), a, r_sum,
done)
replay_memory.add_exp(experience)
# Do training batch update
if (global_steps >= OBSERVE_STEPS):
S, A_, R, D = replay_memory.sample(BATCH_SIZE)
y2 = sess.run(Y, feed_dict={X: S[:, :, :, -4:]})
q = R + (1 - D) * GAMMA * np.max(y2, axis=1)
_, batch_loss = sess.run(
[train_op, L],
feed_dict={
X: S[:, :, :, -5:-1],
Q: q,
A: (A_ - 1),
LR: LEARNING_RATE
})
if (batch_loss == np.nan):
print('nan error, exiting training')
exit()
elif (np.mean(np.max(y2, axis=-1)) > 1e2):
print('unstable Q value, exiting training')
exit()
# Print updates
print(
'Episode: {}/{},\tframe: {},\tscore: {},\t<max(Q)>: {:.3e},\nmax(Q): {:.3e},\taction: {},\tcurrent std(Q)/mean(Q): {:.3e}'
.format(episode + 1, max_episodes,
(frame + 1) * (STEPS_TO_SKIP + 1),
episode_score, np.mean(np.max(y2,
axis=-1)),
np.max(y), action_map[a],
np.std(y) / np.mean(y)))
# Plot frame-by-frame metrics
if avg_reward is None:
avg_reward = r_sum
else:
avg_reward = (1 -
np.exp(-1 / 500)) * r_sum + np.exp(
-1 / 500) * avg_reward
if (global_steps % PLOT_EVERY_N_STEPS == 0):
step_list.append(global_steps)
reward_list.append(10 * avg_reward)
y_val = sess.run(Y, feed_dict={X: X_val})
val_Q_list.append(np.mean(np.max(y_val, axis=-1)))
u.plot_metrics(step_list, 'PongMetrics',
'Pong Metrics', 'Global step', '',
(val_Q_list, 'Validation <max(Q)>'),
(reward_list, '10*<R>'))
else:
print('Observation step {}/{}'.format(
global_steps, OBSERVE_STEPS))
# Update state variables
global_steps += 1
frame += 1
# Save parameters at end of episode, plot episode metrics
print('Saving parameters...')
saver.save(sess, SAVE_PATH)
episode_length_list.append(frame * (STEPS_TO_SKIP + 1) / 1000)
if avg_episode_length is None:
avg_episode_length = frame * (STEPS_TO_SKIP + 1)
else:
avg_episode_length = (1 - np.exp(-1 / 10)) * frame * (
STEPS_TO_SKIP + 1) + np.exp(
-1 / 10) * avg_episode_length
avg_episode_length_list.append(avg_episode_length / 1000)
toc = time.time()
episode_time_list.append((toc - tic) / 60)
episode_score_list['player'].append(episode_score[0])
episode_score_list['computer'].append(episode_score[1])
u.plot_metrics(range(episode + 1), 'EpisodeLength',
'Episode Length', 'Episode', 'Steps/1000',
(episode_length_list, 'Steps/episode'),
(avg_episode_length_list, 'Average'))
u.plot_metrics(range(episode + 1), 'EpisodeScore',
'Episode Score', 'Episode', 'Score',
(episode_score_list['player'], 'Player'),
(episode_score_list['computer'], 'Computer'))
u.plot_metrics(range(episode + 1), 'EpisodeTime',
'Episode time', 'Episode', 'Time (min)',
(episode_time_list, 'Episode time'))
def main():
utils.local_css("css/styles.css")
st.title("Heart Disease Prediction - Manual Parameter Tuner")
st.sidebar.title("Manual Parameter Tuning")
st.markdown("### Machine Learning is not only about the algorithms you use but also about the different Parameters "
"assigned to each of them. The final model is heavily affected by the parameters used for a specific "
"algorithm. "
"\nThis interactive web app will help you to explore the various parameters of different ML algorithms."
"\nThe different ML models presented here are:"
"\n* Logistic Regression"
"\n* Support Vector Classifier"
"\n* k-Nears Neighbour Classifier"
"\n* Decision Tree Classifier"
"\n* Random Forest Classifier"
"\n* Gradient Boosting Classifier"
"\n* XGBoost Classifier"
"\n### The dataset used here is the **Framingham** Coronary Heart Disease dataset publicly available "
"at [Kaggle](https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset)."
"\n## About the Dataset:"
"\nThe **Framingham** dataset is from an ongoing cardiovascular study"
" on residents of the town of Framingham, Massachusetts. The classification goal is "
"to predict whether the patient has 10-year risk of future coronary heart disease (CHD). The dataset "
"provides the patients’ information. It includes over 4,240 records and 15 attributes."
"\n ### Even after optimizing parameters, the model would only work properly if accurate data is provided to it."
" So, through this web app, the users will be able to get a feel of hyperparameter tuning but only on this specific dataset."
"\n ## Head to the *Manual Parameter Tuning* section to get started!")
st.sidebar.markdown("Manually select the model you want to view and use the interactive text boxes, sliding bars "
"and buttons to tune the respective models. More than one options are provided for each model"
" and you can view and gain insight on how hyper-parameter tuning works. Enjoy exploring!")
data = pd.read_csv("Dataset/framingham.csv")
data = utils.preprocess(data)
st.sidebar.markdown("\n#### Exploratory Data Analysis:")
viz_list = st.sidebar.multiselect("(Be sure to Clear off all the selected options here before moving on for faster response)",
('Categorical Visualisation',
'Numerical Visualisation',
'sysBP and diaBP Visualisation'))
utils.visualize(viz_list, data)
if st.sidebar.checkbox("View raw and preprocessed data", False):
st.subheader("Raw-preprocessed Data")
st.write(data)
st.sidebar.markdown("\n#### Feature Selection:")
feature = st.sidebar.radio("Feature selection using chi-squared test", ("Don't select features",
"Select Features"), key='feature')
if feature == "Don't select features":
st.markdown("### Feature Selection is not done!")
else:
st.markdown("Best 10 features along with their chi-squared score")
score, data = utils.feature_selection(data)
st.write(score)
if st.sidebar.checkbox("Plot Feature Selection", False):
utils.plot_feature_selection(score)
train_x, test_x, train_y, test_y = utils.split_and_scale(data)
class_names = ["Has Heart Disease", "Doesn't have Heart Disease"]
st.sidebar.subheader("Choose Classifier")
classifier = st.sidebar.selectbox("Classifier", ("Logistic Regression",
"Support Vector Classifier",
"k-Nears Neighbour Classifier",
"Decision Tree Classifier",
"Random Forest Classifier",
"Gradient Boosting Classifier",
"XGBoost Classifier"))
if classifier == "Logistic Regression":
st.sidebar.subheader("Model Hyperparameters")
C = st.sidebar.number_input("C (Regularization parameter)", 0.01, 10.0, step=0.01, key='Lr')
max_iter = st.sidebar.slider("Maximum no. of Iterations", 100, 500, key='max_iter')
metrics = st.sidebar.multiselect("What matrix to plot?", ("Confusion Matrix", "ROC Curve",
"Precision-Recall Curve"))
if st.sidebar.button("Classify", key="classify"):
st.subheader("Logistic Regression Results")
y_pred, accuracy, models = model.LR(train_x, test_x, train_y, test_y, C=C, max_iter=max_iter)
st.write("Accuracy: ", accuracy.round(3))
st.write("Precision: ", precision_score(test_y, y_pred, labels=class_names).round(3))
st.write("Recall: ", recall_score(test_y, y_pred, labels=class_names).round(3))
utils.plot_metrics(metrics, models, test_x, test_y, class_names)
if classifier == "Support Vector Classifier":
st.sidebar.subheader("Model Hyperparameters")
C = st.sidebar.number_input("C (Regularization parameter)", 0.01, 10.0, step=0.01, key='C')
gamma = st.sidebar.radio("Gamma (for non linear hyperplanes)", ("auto", "scale"), key='gamma')
kernel = st.sidebar.radio("Kernel (type of hyperplane)", ("linear", "rbf", "poly"), key='kernel')
degree = 3
if kernel == 'poly':
degree = st.sidebar.number_input("Degree of the polynomial used to find the hyperplane", 1, 10, step=1,
key='degree')
metrics = st.sidebar.multiselect("What matrix to plot?", ("Confusion Matrix", "ROC Curve",
"Precision-Recall Curve"))
if st.sidebar.button("Classify", key="classify"):
st.subheader("Support Vector Classification Results")
y_pred, accuracy, models = model.SVM(train_x, test_x, train_y, test_y, C=C, gamma=gamma, kernel=kernel,
degree=degree)
st.write("Accuracy: ", accuracy.round(3))
st.write("Precision: ", precision_score(test_y, y_pred, labels=class_names).round(3))
st.write("Recall: ", recall_score(test_y, y_pred, labels=class_names).round(3))
utils.plot_metrics(metrics, models, test_x, test_y, class_names)
if classifier == "k-Nears Neighbour Classifier":
st.sidebar.subheader("Model Hyperparameters")
n = st.sidebar.number_input("n_neighbors (Number of nearest neighbors)", 1, 20, step=1, key='n')
leaf_size = st.sidebar.slider("Leaf Size", 10, 200, key='leaf_size')
algorithm = st.sidebar.radio("Algorithm to use", ("ball_tree", "kd_tree", "auto"), key='algorithm')
metrics = st.sidebar.multiselect("What matrix to plot?", ("Confusion Matrix", "ROC Curve",
"Precision-Recall Curve"))
if st.sidebar.button("Classify", key="classify"):
st.subheader("kNN Classification Results")
y_pred, accuracy, models = model.KNN(train_x, test_x, train_y, test_y, n=n, leaf_size=leaf_size,
algorithm=algorithm)
st.write("Accuracy: ", accuracy.round(3))
st.write("Precision: ", precision_score(test_y, y_pred, labels=class_names).round(3))
st.write("Recall: ", recall_score(test_y, y_pred, labels=class_names).round(3))
utils.plot_metrics(metrics, models, test_x, test_y, class_names)
if classifier == "Decision Tree Classifier":
criterion = st.sidebar.radio("Criterion of splitting trees", ("gini", "entropy"), key='criterion')
max_depth = st.sidebar.slider("Max depth of the tree", 1, 50, key='amx_depth')
min_samples_leaf = st.sidebar.number_input("Minimum Leaf Samples", 1, 10, step=1, key='min_samples_leaf')
max_features = st.sidebar.radio("No. of features to consider during best split", ("auto", "sqrt", "log2"),
key='max_features')
metrics = st.sidebar.multiselect("What matrix to plot?", ("Confusion Matrix", "ROC Curve",
"Precision-Recall Curve"))
if st.sidebar.button("Classify", key="classify"):
st.subheader("Decision Tree Classification Results")
y_pred, accuracy, models = model.DT(train_x, test_x, train_y, test_y, criterion=criterion,
max_depth=max_depth,
leaf=min_samples_leaf, max_features=max_features)
st.write("Accuracy: ", accuracy.round(3))
st.write("Precision: ", precision_score(test_y, y_pred, labels=class_names).round(3))
st.write("Recall: ", recall_score(test_y, y_pred, labels=class_names).round(3))
utils.plot_metrics(metrics, models, test_x, test_y, class_names)
if classifier == "Random Forest Classifier":
st.sidebar.subheader("Model Hyperparameters")
n_estimators = st.sidebar.slider("Number of Trees in the Random Forest", 100, 4000, key='n_estimators')
max_depth = st.sidebar.number_input("The maximum depth of the tree", 1, 100, step=5, key='max_depth')
bootstrap = st.sidebar.radio("Bootstrap samples when building trees", ("True", "False"), key='bootstrap')
metrics = st.sidebar.multiselect("What matrix to plot?", ("Confusion Matrix", "ROC Curve",
"Precision-Recall Curve"))
if st.sidebar.button("Classify", key="classify"):
st.subheader("Random Forest Classification Results")
y_pred, accuracy, models = model.RF(train_x, test_x, train_y, test_y, n_estimators=n_estimators,
max_depth=max_depth, bootstrap=bootstrap)
st.write("Accuracy: ", accuracy.round(3))
st.write("Precision: ", precision_score(test_y, y_pred, labels=class_names).round(3))
st.write("Recall: ", recall_score(test_y, y_pred, labels=class_names).round(3))
utils.plot_metrics(metrics, models, test_x, test_y, class_names)
if classifier == "Gradient Boosting Classifier":
st.sidebar.subheader("Model Hyperparameters")
n_estimators = st.sidebar.slider("Number of Trees in the Gradient Boost ensemble", 100, 4000,
key='n_estimators')
max_depth = st.sidebar.number_input("The maximum depth of the tree", 1, 100, step=5, key='max_depth')
learning_rate = st.sidebar.number_input("Learning Rate", 0.01, 10.0, step=0.01, key='learning_rate')
warm_start = st.sidebar.radio("Reuse previous solution for more ensemble", ("True", "False"), key='warm_start')
metrics = st.sidebar.multiselect("What matrix to plot?", ("Confusion Matrix", "ROC Curve",
"Precision-Recall Curve"))
if st.sidebar.button("Classify", key="classify"):
st.subheader("Gradient Boosting Classification Results")
y_pred, accuracy, models = model.GBC(train_x, test_x, train_y, test_y, n_estimators=n_estimators,
max_depth=max_depth, learning_rate=learning_rate,
warm_start=warm_start)
st.write("Accuracy: ", accuracy.round(3))
st.write("Precision: ", precision_score(test_y, y_pred, labels=class_names).round(3))
st.write("Recall: ", recall_score(test_y, y_pred, labels=class_names).round(3))
utils.plot_metrics(metrics, models, test_x, test_y, class_names)
if classifier == "XGBoost Classifier":
st.sidebar.subheader("Model Hyperparameters")
n_estimators = st.sidebar.slider("Number of Trees in the XGBoost ensemble", 100, 4000, key='n_estimators')
max_depth = st.sidebar.number_input("The maximum depth of the tree", 1, 100, step=5, key='max_depth')
eta = st.sidebar.number_input("Learning Rate", 0.01, 10.0, step=0.01, key='eta')
colsample_bytree = st.sidebar.number_input("Percentage of features used per tree", 0.01, 1.0, step=0.01,
key='colsample_bytree')
reg_alpha = st.sidebar.number_input("L1 regularization on leaf weights", 1, 10, step=1, key='reg_alpha')
reg_lambda = st.sidebar.number_input("L2 regularization on leaf weights", 1, 10, step=1, key='reg_lambda')
metrics = st.sidebar.multiselect("What matrix to plot?", ("Confusion Matrix", "ROC Curve",
"Precision-Recall Curve"))
if st.sidebar.button("Classify", key="classify"):
st.subheader("Extreme Gradient Boosting(XGBoost) Classification Results")
y_pred, accuracy, models = model.XGB(train_x, test_x, train_y, test_y, n_estimators=n_estimators,
max_depth=max_depth, eta=eta, colsample_bytree=colsample_bytree,
reg_alpha=reg_alpha, reg_lambda=reg_lambda)
st.write("Accuracy: ", accuracy.round(3))
st.write("Precision: ", precision_score(test_y, y_pred, labels=class_names).round(3))
st.write("Recall: ", recall_score(test_y, y_pred, labels=class_names).round(3))
utils.plot_metrics(metrics, models, test_x, test_y, class_names)
def train_subj(subj, save_dir, N_epoch=20, dev_set='AVG_100', plot=1):
#%% load saved data (.mat) from MATLAB
#subj = 's21'
# (1)train set:
# filename1 = 'EEG/SNR3D_set_s1.mat'
filename1 = 'DL_trainset/AVG_ram100/' + subj + '.mat'
file = loadmat(filename1)
data1 = file['SNR3D_arr']
# (2) dev set:
# AVG_spectrum_s1 = 'EEG/SNR3D_spectrum_s1.mat'
AVG_spectrum_s1 = 'DL_trainset/AVG96/' + subj + '.mat'
file = loadmat(AVG_spectrum_s1)
data1_dev = file['SNR3D']
print('data1_dev:', data1_dev.shape)
# check shape of INPUT data
r, c = data1.shape
for i in range(c):
print(data1[0, i].shape)
#%% orginal labels -> binary labels
labels_full = [
4, 6, 27, 33, 35, 37, 39, 43, 45, 66, 72, 76, 78, 99, 105, 111, 117
]
labels_subset = [6, 33, 37, 39, 43, 66, 72, 78, 99, 117] # (1)
labels_subset2 = [6, 33, 37, 39, 43, 45, 66, 72, 76, 78, 99, 117] # (2)
labels_subset3 = [6, 33, 35, 37, 39, 43, 45, 66, 72, 76, 78, 99,
117] # (3)
# choose one annotation from above !
labels_2use = labels_full
labels_org = np.array(labels_2use) - 1 # python starts from 0
labels_bi = xloc2binaryvec(labels_org) # binary code label: 1 and 0
#%% prepare {x_train, y_train, x_test, y_test}
# option B: use AVG- [50, 60, 70, 80, 90] trials for train
x_train = np.concatenate(
(data1[0, 0], data1[0, 1], data1[0, 2], data1[0, 3], data1[0, 4]),
axis=0)
if dev_set == 'AVG_100': # saved in folder named 'saved_models2'
x_test_100tr = data1_dev[-1] # dev set: AVG-100 trials (1,:,:)
x_test = np.reshape(x_test_100tr,
(1, x_test_100tr.shape[0], x_test_100tr.shape[1]))
elif dev_set == 'AVG_50_100': # saved in folder named 'mat3'
# dev set: AVG 5-100 trials
x_test = data1_dev[-50:, :, :] # (50-100,:,:)
elif dev_set == 'AVG_5_100': # whole dev set: 'AVG_5_100' - saved in folder named 'saved_models'
x_test = data1_dev # (5-100, :,:)
print('dev_set: ', x_test.shape)
# normalize X to [0 - 1]
x_train = normalize_matrix_1(x_train)
x_test = normalize_matrix_1(x_test)
# repeating Y (labels_bi) the number of times given by number of samples
y_train = np.tile(labels_bi, (len(x_train), 1)) # (10k,120)
y_test = np.tile(labels_bi, (len(x_test), 1)) # (100,120)
# shuffle [x_train, y_train] 100x along the dimenstion of frequency '1-120'
x_train, y_train = shuffle_trainset(x_train, y_train)
#%% reshape for CNN: not neccesary, tf.keras will take care if this
# x_train = x_train.reshape(x_train.shape[0], 12, 120, 1)
# x_test = x_test.reshape(x_test.shape[0], 12, 120, 1)
#%% define a model
tf.keras.backend.clear_session()
def create_model():
# (C) CNN model (~2 conv layers): ~ 230k paras
inputs = keras.Input(shape=(
60, 120,
1)) # 1 is needed here to keep the same dim with next conv2D layer
x = layers.experimental.preprocessing.Rescaling(1.0 / 1)(inputs)
x = layers.Conv2D(filters=8,
kernel_size=(3, 3),
padding='same',
activation="relu")(x)
x = layers.MaxPooling2D(pool_size=(3, 3),
strides=(1, 1),
padding='same')(
x) # stride =1, not =3 be default
x = layers.Dropout(.2)(x)
x = layers.Conv2D(filters=4,
kernel_size=(3, 3),
padding='same',
activation="relu")(x)
x = layers.MaxPooling2D(pool_size=(2, 2),
strides=(1, 1),
padding='same')(
x) # stride =1, not =2 be default
x = layers.Dropout(.2)(x)
x = layers.Flatten()(x)
x = layers.Dense(1024,
activation="relu")(x) # it does not further improve!
outputs = layers.Dense(120, activation='sigmoid')(x)
model = keras.Model(inputs, outputs)
loss = tf.keras.losses.BinaryCrossentropy()
# optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-07)
# METRICS = [
# keras.metrics.TruePositives(name='tp'),
# keras.metrics.FalsePositives(name='fp'),
# keras.metrics.TrueNegatives(name='tn'),
# keras.metrics.FalseNegatives(name='fn'),
# keras.metrics.BinaryAccuracy(name='accuracy'),
# keras.metrics.Precision(name='precision'),
# keras.metrics.Recall(name='recall'),
# keras.metrics.AUC(name='auc'),
# ]
METRICS = [
keras.metrics.BinaryAccuracy(name='accuracy'),
keras.metrics.Precision(name='precision'),
keras.metrics.Recall(name='recall'),
keras.metrics.AUC(name='auc',
curve='PR'), # curve='ROC' by default
]
model.compile(
optimizer=optimizer,
loss=loss,
metrics=METRICS,
)
return model
#%% Build a model
# Create a basic model instance
model = create_model()
# show model
model.summary()
#%% train
# N_epoch = 20
# (callback 1) for using TensorBoard
callback_log = [keras.callbacks.TensorBoard(log_dir='./logs')]
# Launch TensorBoard from the command line (1st cd: folder of this proj)
# in cmd type: tensorboard --logdir logs
# (callback 2) Create a callback that saves the model's weights
# only model that achieves larger 'val_auc' is saved.
checkpoint_path = "checkpoints/my_ckpt"
callback_cp = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
monitor='val_auc',
mode='max',
save_best_only=True,
verbose=1)
start_time = time.time()
# Train the model from Numpy data input
history = model.fit(x_train,
y_train,
validation_data=(x_test, y_test),
batch_size=32,
epochs=N_epoch,
callbacks=[callback_log, callback_cp])
print('elapsed_time:', time.time() - start_time) # =203 s for 100 epochs
#%% plot learning curves - [loss, AUC, etc.]
if plot == 1:
plt.figure(figsize=(12, 10))
plot_loss(history, "loss", 2)
# plot metrics over epochs
plt.figure(figsize=(12, 10))
plot_metrics(history)
#%% Evaluate the saved 'best model weights' by callback_func
# Create a basic model instance
model = create_model()
# Display the model's architecture
model.summary()
# Evaluate the initial model
loss, acc, _, _, _ = model.evaluate(x_test, y_test, verbose=2)
print("Untrained model, accuracy: {:5.2f}%".format(100 * acc))
# Re-load the model weights from the saved checkpoints
model.load_weights(checkpoint_path) # Loads the weights
# Evaluate the saved model
loss, acc, _, _, _ = model.evaluate(x_test, y_test, verbose=2)
print("saved model, accuracy: {:5.2f}%".format(100 * acc))
# save the entire trained model (not only weights in checkpoints)
# choose the path for saving
# save_dir = os.path.join(os.getcwd(), 'saved_models')
# if not os.path.isdir(save_dir):
# os.makedirs(save_dir)
model_name = subj + '_best_trained_model.h5'
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
#%% test the best_trained_model on x_test (AVG-100 trials) of day1 EEG
if plot == 1:
predictions = model.predict(x_test)
predictions_mean = np.mean(predictions, axis=0)
labels2plt = labels_org
plt.figure(figsize=(16, 9))
plt.imshow(predictions, cmap='gray')
plot1bar(labels2plt, L=96)
plt.xlabel('frequency')
plt.ylabel('All test samples')
plt.title('prediction on AVG 90 trials (with Ann)')
plt.figure(figsize=(16, 9))
plt.plot(predictions_mean)
plot1bar(labels2plt, L=2)
plt.grid(True)
plt.xlabel('frequency')
plt.ylabel('probability of ASSR')
plt.title('Mean prediction')
# In[ ]:
with open('results.log', 'w') as f:
lr=0.001
opt = RAdam(network.get_trainable_parameters(lr))
# opt = torch.optim.SGD(network.get_trainable_parameters(lr), lr=lr, momentum=0.9)
res_metrics = network.fit(
load_data,
epochs=nb_epochs,
optimizer=opt,
dataset_size=dataset_size,
result_file=f,
save_checkpoints=True
)
plot_metrics(res_metrics, save_plot_path='./metrics_' if SAVE_PLOTS else None)
network.save('save_network.net')
# network.load('save_network.net')
# In[ ]:
plot_one_batch(network)
print_and_plot_accuracy_metrics(
network,
load_data('acc_train'),
load_data('acc_test'),
save_plot_path='./accuracy_' if SAVE_PLOTS else None
)
def train(G, max_episodes, save_path):
'''
Trains a DQN to play pong.
'''
# Define some constants, lists, metrics, etc
action_map = {1: 'x', 2: '^', 3: 'v'} # Stay, up, down
replay_memory = u.ReplayMemory(max_exp_len=REPLAY_MEM_LEN)
step_list = []
reward_list = []
val_Q_list = []
episode_length_list = []
avg_episode_length_list = []
episode_score_list = {'player': [], 'computer': []}
X_val = u.load_validation_screens()
# Initialize the Pong gym environment, set seeds
env = gym.make('Pong-v0')
np.random.seed(SEED)
tf.set_random_seed(SEED)
plt.ioff()
# Gather screens
# Initialize computational graph
with G.as_default():
# Get input/output tensors
X = G.get_tensor_by_name('X:0')
Y = G.get_tensor_by_name('Y:0')
# Append loss function to graph
Q = tf.placeholder(dtype=tf.float32, shape=[None], name='Q')
A = tf.placeholder(dtype=tf.int32, shape=[None], name='A')
mask = tf.one_hot(A, depth=3, dtype=tf.float32, axis=-1)
L = tf.reduce_mean(tf.square(tf.reduce_sum(mask * Y, axis=-1) - Q),
name='L')
# Define optimizer, training op, gradient clipping, etc.
if not RELOAD_PARAMETERS:
optimizer = tf.train.AdamOptimizer(LEARNING_RATE, name='Adam')
else:
optimizer = G.get_operation_by_name('Adam')
saver = tf.train.Saver()
# Initialize TF session
with tf.Session() as sess:
# Reload/initialize variables
if RELOAD_PARAMETERS:
print('Reloading from last checkpoint...')
saver.restore(sess, save_path)
else:
print('Initializing variables...')
gradients, variables = zip(*optimizer.compute_gradients(L))
train_op = optimizer.apply_gradients(zip(gradients, variables))
sess.run(tf.global_variables_initializer())
# Iterate over episodes
global_steps = 0
for episode in range(max_episodes):
obs = u.preprocess_image(env.reset())
for i in range(3):
replay_memory.add_frame(
np.zeros((160 // DOWNSAMPLE, 160 // DOWNSAMPLE)))
replay_memory.add_frame(obs)
# Iterate over frames
done = False
frame = 0
episode_score = [0, 0]
while not done:
if (global_steps >= OBSERVE_STEPS):
# Feed state into DQN
s = np.stack(
[replay_memory.frames[i] for i in range(-4, 0)],
axis=-1).reshape(1, 160 // DOWNSAMPLE,
160 // DOWNSAMPLE, 4)
y = sess.run(Y, feed_dict={X: s})
# Decide on action
epsilon = max(
MAX_EPSILON *
(1 - global_steps / EPSILON_ANNEALING_STEPS),
MIN_EPSILON)
if (np.random.rand() < epsilon):
a = np.random.choice([1, 2, 3])
else:
a = np.argmax(y) + 1
else:
a = np.random.choice([1, 2, 3])
# Take action, observe environment, reward
obs, r, done, _ = env.step(a)
r_sum = r
for i in range(STEPS_TO_SKIP):
obs, r, done_temp, _ = env.step(1)
r_sum += r
if done_temp == True:
done = True
if r_sum > 0:
episode_score[0] += r_sum
elif r_sum < 0:
episode_score[1] -= r_sum
# Add new state/reward to replay memory
replay_memory.add_frame(u.preprocess_image(obs))
experience = (np.stack(list(replay_memory.frames),
axis=-1), a, r_sum, done)
replay_memory.add_exp(experience)
# Do training batch update
if (global_steps >= OBSERVE_STEPS):
S, A_, R, D = replay_memory.sample(BATCH_SIZE)
y2 = sess.run(Y, feed_dict={X: S[:, :, :, -4:]})
q = R + (1 - D) * GAMMA * np.max(y2, axis=1)
_, batch_loss = sess.run([train_op, L],
feed_dict={
X: S[:, :, :, -5:-1],
Q: q,
A: (A_ - 1)
})
if (batch_loss == np.nan):
print('nan error, exiting training')
exit()
elif (np.mean(np.max(y2, axis=-1)) > 1e2):
print('unstable Q value, exiting training')
exit()
# Print updates
print(
'Episode: {}/{},\tframe: {},\treward: {},\t<max(Q)>: {:.3e},\nmax(Q): {:.3e},\taction: {},\tcurrent std(Q)/mean(Q): {:.3e}'
.format(episode + 1, max_episodes, frame + 1,
int(r_sum), np.mean(np.max(y2, axis=-1)),
np.max(y), action_map[a],
np.std(y) / np.mean(y)))
# Plot frame-by-frame metrics
if global_steps == 0:
avg_reward = r_sum
else:
avg_reward = (1 -
np.exp(-1 / 500)) * r_sum + np.exp(
-1 / 500) * avg_reward
if (global_steps % PLOT_EVERY_N_STEPS == 0):
step_list.append(global_steps)
reward_list.append(10 * avg_reward)
y_val = sess.run(Y, feed_dict={X: X_val})
val_Q_list.append(np.mean(np.max(y_val, axis=-1)))
u.plot_metrics(step_list, 'PongMetrics',
'Pong Metrics', 'Global step', '',
(val_Q_list, 'Validation <max(Q)>'),
(reward_list, '10*<R>'))
else:
print('Observation step {}/{}'.format(
global_steps, OBSERVE_STEPS))
# Update state variables
global_steps += 1
frame += 1
# Save parameters at end of episode, plot episode metrics
saver.save(sess, SAVE_PATH)
episode_length_list.append(frame * (STEPS_TO_SKIP + 1) / 1000)
if episode == 0:
avg_episode_length = frame * (STEPS_TO_SKIP + 1)
else:
avg_episode_length = (1 - np.exp(-1 / 10)) * frame * (
STEPS_TO_SKIP + 1) + np.exp(
-1 / 10) * avg_episode_length
avg_episode_length_list.append(avg_episode_length / 1000)
episode_score_list['player'].append(episode_score[0])
episode_score_list['computer'].append(episode_score[1])
u.plot_metrics(range(episode + 1), 'EpisodeLength',
'Episode Length', 'Episode', 'Length/1000',
(episode_length_list, 'Episode length'),
(avg_episode_length_list, 'Average'))
u.plot_metrics(range(episode + 1), 'EpisodeScore',
'Episode Score', 'Episode', 'Score',
(episode_score_list['player'], 'Player'),
(episode_score_list['computer'], 'Computer'))