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Commit fa1bf0b6 authored by tomrink's avatar tomrink
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......@@ -23,10 +23,10 @@ else:
NumLogits = NumClasses
BATCH_SIZE = 64
NUM_EPOCHS = 60
NUM_EPOCHS = 80
TRACK_MOVING_AVERAGE = False
EARLY_STOP = False
EARLY_STOP = True
NOISE_TRAINING = False
NOISE_STDDEV = 0.10
......@@ -366,7 +366,7 @@ class ESPCN:
self.get_evaluate_dataset(idxs)
def build_espcn(self):
def build_espcn(self, do_drop_out=False, do_batch_norm=False, drop_rate=0.5):
print('build_cnn')
# padding = "VALID"
padding = "SAME"
......@@ -389,12 +389,20 @@ class ESPCN:
if NOISE_TRAINING:
conv = tf.keras.layers.GaussianNoise(stddev=NOISE_STDDEV)(conv)
if do_drop_out:
conv = tf.keras.layers.Dropout(drop_rate)(conv)
if do_batch_norm:
conv = tf.keras.layers.BatchNormalization()(conv)
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
print(conv.shape)
if do_drop_out:
conv = tf.keras.layers.Dropout(drop_rate)(conv)
if do_batch_norm:
conv = tf.keras.layers.BatchNormalization()(conv)
conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
print(conv.shape)
# conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(conv)
......@@ -405,12 +413,21 @@ class ESPCN:
# conv = tf.keras.layers.LeakyReLU()(conv)
# print(conv.shape)
if do_drop_out:
conv = tf.keras.layers.Dropout(drop_rate)(conv)
if do_batch_norm:
conv = tf.keras.layers.BatchNormalization()(conv)
conv = tf.keras.layers.Conv2D(num_filters // 2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
print(conv.shape)
if do_drop_out:
conv = tf.keras.layers.Dropout(drop_rate)(conv)
if do_batch_norm:
conv = tf.keras.layers.BatchNormalization()(conv)
conv = tf.keras.layers.Conv2D(num_filters // 2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
conv = tf.keras.layers.BatchNormalization()(conv)
print(conv.shape)
# conv = tf.keras.layers.Conv2D(4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
......@@ -419,7 +436,11 @@ class ESPCN:
conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=2, padding=padding, activation=activation)(conv)
print(conv.shape)
self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability', activation=tf.nn.sigmoid)(conv)
conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
print(conv.shape)
#self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability', activation=tf.nn.sigmoid)(conv)
self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability')(conv)
# conv = tf.nn.depth_to_space(conv, block_size=2)
# conv = tf.keras.layers.Activation(activation=activation)(conv)
......@@ -755,8 +776,9 @@ class ESPCN:
pred = self.model([data])
self.test_probs = pred
pred = pred.numpy()
return denormalize(pred, param, mean_std_dct[param])
return denormalize(pred, param, mean_std_dct)
def run(self, directory):
train_data_files = glob.glob(directory+'data_train*.npy')
......@@ -780,10 +802,11 @@ class ESPCN:
def run_evaluate(self, nda_lr, param, ckpt_dir):
# self.setup_eval_pipeline(filename)
self.num_data_samples = 80000
self.build_model()
self.build_training()
self.build_evaluation()
self.do_evaluate(nda_lr, param, ckpt_dir)
return self.do_evaluate(nda_lr, param, ckpt_dir)
if __name__ == "__main__":
......
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