diff --git a/modules/deeplearning/icing.py b/modules/deeplearning/icing.py index 24471ddb36e76d2c8b4087f039904dcc2321aeaf..730490a2672b599b8cf3e2a899d4f0c499aa80af 100644 --- a/modules/deeplearning/icing.py +++ b/modules/deeplearning/icing.py @@ -14,11 +14,11 @@ LOG_DEVICE_PLACEMENT = False CACHE_DATA_IN_MEM = True -PROC_BATCH_SIZE = 60 +PROC_BATCH_SIZE = 10240 PROC_BATCH_BUFFER_SIZE = 50000 NumLabels = 1 -BATCH_SIZE = 512 -NUM_EPOCHS = 200 +BATCH_SIZE = 256 +NUM_EPOCHS = 20 TRACK_MOVING_AVERAGE = False @@ -350,7 +350,7 @@ class IcingIntensityNN: else: flat = self.X_img n_hidden = self.X_img.shape[1] - n_hidden = 100 + n_hidden = 40 fac = 1 @@ -358,11 +358,11 @@ class IcingIntensityNN: fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_2') - fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_3') + #fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_3') - fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_4') + #fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_4') - fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_5') + #fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_5') fc = tf.keras.layers.Dense(n_hidden, activation=activation)(fc) fc = tf.keras.layers.BatchNormalization()(fc) @@ -402,8 +402,8 @@ class IcingIntensityNN: self.initial_learning_rate = initial_learning_rate def build_evaluation(self): - self.train_accuracy = tf.keras.metrics.MeanAbsoluteError(name='train_accuracy') - self.test_accuracy = tf.keras.metrics.MeanAbsoluteError(name='test_accuracy') + self.train_accuracy = tf.keras.metrics.BinaryAccuracy(name='train_accuracy') + self.test_accuracy = tf.keras.metrics.BinaryAccuracy(name='test_accuracy') self.train_loss = tf.keras.metrics.Mean(name='train_loss') self.test_loss = tf.keras.metrics.Mean(name='test_loss')