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Commit 565038f5 authored by tomrink's avatar tomrink
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modules/deeplearning/espcn.py
+ 4
− 69
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......@@ -341,9 +341,9 @@ class ESPCN:
print('build_cnn')
padding = "SAME"
# activation = tf.nn.relu
# activation = tf.nn.elu
activation = tf.nn.leaky_relu
# activation = tf.nn.leaky_relu
activation = tf.nn.relu
# kernel_initializer = 'glorot_uniform'
kernel_initializer = 'he_uniform'
momentum = 0.99
......@@ -392,10 +392,6 @@ class ESPCN:
print(self.logits.shape)
def build_training(self):
# if NumClasses == 2:
# self.loss = tf.keras.losses.BinaryCrossentropy(from_logits=False) # for two-class only
# else:
# self.loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) # For multi-class
self.loss = tf.keras.losses.MeanSquaredError() # Regression
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
......@@ -425,20 +421,6 @@ class ESPCN:
self.train_loss = tf.keras.metrics.Mean(name='train_loss')
self.test_loss = tf.keras.metrics.Mean(name='test_loss')
# if NumClasses == 2:
# self.train_accuracy = tf.keras.metrics.BinaryAccuracy(name='train_accuracy')
# self.test_accuracy = tf.keras.metrics.BinaryAccuracy(name='test_accuracy')
# self.test_auc = tf.keras.metrics.AUC(name='test_auc')
# self.test_recall = tf.keras.metrics.Recall(name='test_recall')
# self.test_precision = tf.keras.metrics.Precision(name='test_precision')
# self.test_true_neg = tf.keras.metrics.TrueNegatives(name='test_true_neg')
# self.test_true_pos = tf.keras.metrics.TruePositives(name='test_true_pos')
# self.test_false_neg = tf.keras.metrics.FalseNegatives(name='test_false_neg')
# self.test_false_pos = tf.keras.metrics.FalsePositives(name='test_false_pos')
# else:
# self.train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
# self.test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
@tf.function
def train_step(self, mini_batch):
inputs = [mini_batch[0]]
......@@ -469,14 +451,6 @@ class ESPCN:
self.test_loss(t_loss)
self.test_accuracy(labels, pred)
# if NumClasses == 2:
# self.test_auc(labels, pred)
# self.test_recall(labels, pred)
# self.test_precision(labels, pred)
# self.test_true_neg(labels, pred)
# self.test_true_pos(labels, pred)
# self.test_false_neg(labels, pred)
# self.test_false_pos(labels, pred)
def predict(self, mini_batch):
inputs = [mini_batch[0]]
......@@ -493,14 +467,6 @@ class ESPCN:
def reset_test_metrics(self):
self.test_loss.reset_states()
self.test_accuracy.reset_states()
# if NumClasses == 2:
# self.test_auc.reset_states()
# self.test_recall.reset_states()
# self.test_precision.reset_states()
# self.test_true_neg.reset_states()
# self.test_true_pos.reset_states()
# self.test_false_neg.reset_states()
# self.test_false_pos.reset_states()
def get_metrics(self):
recall = self.test_recall.result()
......@@ -533,12 +499,6 @@ class ESPCN:
step = 0
total_time = 0
best_test_loss = np.finfo(dtype=np.float).max
best_test_acc = 0
best_test_recall = 0
best_test_precision = 0
best_test_auc = 0
best_test_f1 = 0
best_test_mcc = 0
if EARLY_STOP:
es = EarlyStop()
......@@ -574,20 +534,9 @@ class ESPCN:
for mini_batch_test in tst_ds:
self.test_step(mini_batch_test)
# if NumClasses == 2:
# f1, mcc = self.get_metrics()
with self.writer_valid.as_default():
tf.summary.scalar('loss_val', self.test_loss.result(), step=step)
tf.summary.scalar('acc_val', self.test_accuracy.result(), step=step)
# if NumClasses == 2:
# tf.summary.scalar('auc_val', self.test_auc.result(), step=step)
# tf.summary.scalar('recall_val', self.test_recall.result(), step=step)
# tf.summary.scalar('prec_val', self.test_precision.result(), step=step)
# tf.summary.scalar('f1_val', f1, step=step)
# tf.summary.scalar('mcc_val', mcc, step=step)
# tf.summary.scalar('num_train_steps', step, step=step)
# tf.summary.scalar('num_epochs', epoch, step=step)
with self.writer_train_valid_loss.as_default():
tf.summary.scalar('loss_trn', loss.numpy(), step=step)
......@@ -615,25 +564,11 @@ class ESPCN:
self.test_step(mini_batch)
print('loss, acc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy())
# if NumClasses == 2:
# f1, mcc = self.get_metrics()
# print('loss, acc, recall, precision, auc, f1, mcc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),
# self.test_recall.result().numpy(), self.test_precision.result().numpy(), self.test_auc.result().numpy(), f1.numpy(), mcc.numpy())
# else:
# print('loss, acc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy())
print('------------------------------------------------------')
tst_loss = self.test_loss.result().numpy()
if tst_loss < best_test_loss:
best_test_loss = tst_loss
# if NumClasses == 2:
# best_test_acc = self.test_accuracy.result().numpy()
# best_test_recall = self.test_recall.result().numpy()
# best_test_precision = self.test_precision.result().numpy()
# best_test_auc = self.test_auc.result().numpy()
# best_test_f1 = f1.numpy()
# best_test_mcc = mcc.numpy()
ckpt_manager.save()
if EARLY_STOP and es.check_stop(tst_loss):
......@@ -741,9 +676,9 @@ class ESPCN:
def prepare(param_idx=1, filename='/Users/tomrink/data_valid_40.npy'):
nda = np.load(filename)
#nda = nda[:, param_idx, :, :]
# nda = nda[:, param_idx, :, :]
nda_lr = nda[:, param_idx, 2:133:2, 2:133:2]
# nda_lr = resample(x_70, y_70, nda, x_70_2, y_70_2)
# nda_lr = resample(x_134, y_134, nda, x_134_2, y_134_2)
nda_lr = np.expand_dims(nda_lr, axis=3)
return nda_lr
......
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