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| -rw-r--r-- | __pycache__/hyperparameters.cpython-38.pyc | bin | 369 -> 341 bytes | |||
| -rw-r--r-- | __pycache__/losses.cpython-38.pyc | bin | 4473 -> 4395 bytes | |||
| -rw-r--r-- | hyperparameters.py | 2 | ||||
| -rw-r--r-- | losses.py | 2 | ||||
| -rw-r--r-- | save.jpg | bin | 30377 -> 28909 bytes |
5 files changed, 2 insertions, 2 deletions
diff --git a/__pycache__/hyperparameters.cpython-38.pyc b/__pycache__/hyperparameters.cpython-38.pyc Binary files differindex 11bc2070..cb33700b 100644 --- a/__pycache__/hyperparameters.cpython-38.pyc +++ b/__pycache__/hyperparameters.cpython-38.pyc diff --git a/__pycache__/losses.cpython-38.pyc b/__pycache__/losses.cpython-38.pyc Binary files differindex d583a985..7fdcb544 100644 --- a/__pycache__/losses.cpython-38.pyc +++ b/__pycache__/losses.cpython-38.pyc diff --git a/hyperparameters.py b/hyperparameters.py index a0068dd1..71c5325a 100644 --- a/hyperparameters.py +++ b/hyperparameters.py @@ -9,7 +9,7 @@ Number of epochs. If you experiment with more complex networks you might need to increase this. Likewise if you add regularization that slows training. """ -num_epochs = 100 +num_epochs = 2000 """ A critical parameter that can dramatically affect whether training @@ -21,7 +21,7 @@ class YourModel(tf.keras.Model): print(self.content_image.shape, self.style_image.shape) - self.optimizer = tf.keras.optimizers.RMSprop(learning_rate=hp.learning_rate, momentum=hp.momentum) + self.optimizer = tf.keras.optimizers.Adam() self.vgg16 = [ # Block 1 Binary files differ |
