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Commit 95e72119 authored by tomrink's avatar tomrink
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parent 5a66d643
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......@@ -56,7 +56,7 @@ label_params = ['cloud_fraction']
DO_ZERO_OUT = False
label_idx = 3
label_idx = 4
label_param = params[label_idx]
print('data_params: ', data_params)
print('label_params: ', label_params)
......@@ -396,11 +396,11 @@ class SRCNN:
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2', scale=scale)
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale)
# conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale)
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
# conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
# conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, kernel_initializer='he_uniform', padding=padding)(conv_b)
......@@ -416,6 +416,7 @@ class SRCNN:
# 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.MeanAbsoluteError() # Regression
self.loss = tf.keras.losses.MeanSquaredError() # Regression
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
......
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