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Author: netik1020

Image_Classifier/skin_cancer_classification_1.ipynb

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+import numpy as np
+import os
+
+import keras.callbacks as callbacks
+from keras.callbacks import Callback
+
+class SnapshotModelCheckpoint(Callback):
+    """Callback that saves the snapshot weights of the model.
+    Saves the model weights on certain epochs (which can be considered the
+    snapshot of the model at that epoch).
+    Should be used with the cosine annealing learning rate schedule to save
+    the weight just before learning rate is sharply increased.
+    # Arguments:
+        nb_epochs: total number of epochs that the model will be trained for.
+        nb_snapshots: number of times the weights of the model will be saved.
+        fn_prefix: prefix for the filename of the weights.
+    """
+
+    def __init__(self, nb_epochs, nb_snapshots, fn_prefix='Model'):
+        super(SnapshotModelCheckpoint, self).__init__()
+
+        self.check = nb_epochs // nb_snapshots
+        self.fn_prefix = fn_prefix
+
+    def on_epoch_end(self, epoch, logs={}):
+        if epoch != 0 and (epoch + 1) % self.check == 0:
+            filepath = self.fn_prefix + "-%d.h5" % ((epoch + 1) // self.check)
+            self.model.save_weights(filepath, overwrite=True)
+            #print("Saved snapshot at weights/%s_%d.h5" % (self.fn_prefix, epoch))
+
+
+class SnapshotCallbackBuilder:
+    """Callback builder for snapshot ensemble training of a model.
+    Creates a list of callbacks, which are provided when training a model
+    so as to save the model weights at certain epochs, and then sharply
+    increase the learning rate.
+    """
+
+    def __init__(self, nb_epochs, nb_snapshots, init_lr=0.1):
+        """
+        Initialize a snapshot callback builder.
+        # Arguments:
+            nb_epochs: total number of epochs that the model will be trained for.
+            nb_snapshots: number of times the weights of the model will be saved.
+            init_lr: initial learning rate
+        """
+        self.T = nb_epochs
+        self.M = nb_snapshots
+        self.alpha_zero = init_lr
+
+    def get_callbacks(self, model_prefix='Model'):
+        """
+        Creates a list of callbacks that can be used during training to create a
+        snapshot ensemble of the model.
+        Args:
+            model_prefix: prefix for the filename of the weights.
+        Returns: list of 3 callbacks [ModelCheckpoint, LearningRateScheduler,
+                 SnapshotModelCheckpoint] which can be provided to the 'fit' function
+        """
+        if not os.path.exists('weights/'):
+            os.makedirs('weights/')
+
+        callback_list = [callbacks.ModelCheckpoint("weights/%s-Best.h5" % model_prefix, monitor="val_acc",
+                                                    save_best_only=True, save_weights_only=True),
+                         callbacks.LearningRateScheduler(schedule=self._cosine_anneal_schedule),
+                         SnapshotModelCheckpoint(self.T, self.M, fn_prefix='weights/%s' % model_prefix)]
+
+        return callback_list
+
+    def _cosine_anneal_schedule(self, t):
+        cos_inner = np.pi * (t % (self.T // self.M))  # t - 1 is used when t has 1-based indexing.
+        cos_inner /= self.T // self.M
+        cos_out = np.cos(cos_inner) + 1
+        return float(self.alpha_zero / 2 * cos_out)