pruned and non pruned model

callbacks.py

@@ -0,0 +1,113 @@
+'''
+Created on 7 Apr 2017
+
+@author: jkiesele
+'''
+from __future__ import print_function
+
+
+from tensorflow.keras.callbacks import Callback, EarlyStopping,History,ModelCheckpoint,TensorBoard,ReduceLROnPlateau
+# loss per epoch
+from time import time
+from pdb import set_trace
+import json
+
+class newline_callbacks_begin(Callback):
+
+    def __init__(self,outputDir):
+        self.outputDir=outputDir
+        self.loss=[]
+        self.val_loss=[]
+        self.full_logs=[]
+
+    def on_epoch_end(self,epoch, epoch_logs={}):
+        import os
+        lossfile=os.path.join( self.outputDir, 'losses.log')
+        print('\n***callbacks***\nsaving losses to '+lossfile)
+        self.loss.append(epoch_logs.get('loss'))
+        self.val_loss.append(epoch_logs.get('val_loss'))
+        f = open(lossfile, 'w')
+        for i in range(len(self.loss)):
+            f.write(str(self.loss[i]))
+            f.write(" ")
+            f.write(str(self.val_loss[i]))
+            f.write("\n")
+        f.close()    
+        normed = {}
+        for vv in epoch_logs:
+            normed[vv] = float(epoch_logs[vv])
+        self.full_logs.append(normed)
+        lossfile=os.path.join( self.outputDir, 'full_info.log')
+        with open(lossfile, 'w') as out:
+            out.write(json.dumps(self.full_logs))
+
+class newline_callbacks_end(Callback):
+    def on_epoch_end(self,epoch, epoch_logs={}):
+        print('\n***callbacks end***\n')
+
+
+class Losstimer(Callback):
+    def __init__(self, every = 5):
+        self.points = []
+        self.every = every
+
+    def on_train_begin(self, logs):
+        self.start = time()
+
+    def on_batch_end(self, batch, logs):
+        if (batch % self.every) != 0: return
+        elapsed = time() - self.start
+        cop = {}
+        for i, j in logs.items():
+            cop[i] = float(j)
+        cop['elapsed'] = elapsed
+        self.points.append(cop)
+
+class all_callbacks(object):
+    def __init__(self,
+                 stop_patience=10,
+                 lr_factor=0.5,
+                 lr_patience=1,
+                 lr_epsilon=0.001,
+                 lr_cooldown=4,
+                 lr_minimum=1e-5,
+                 outputDir=''):
+
+
+
+        self.nl_begin=newline_callbacks_begin(outputDir)
+        self.nl_end=newline_callbacks_end()
+
+        self.stopping = EarlyStopping(monitor='val_loss', 
+                                      patience=stop_patience, 
+                                      verbose=1, mode='min')
+
+        self.reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=lr_factor, patience=lr_patience, 
+                                mode='min', verbose=1, epsilon=lr_epsilon,
+                                 cooldown=lr_cooldown, min_lr=lr_minimum)
+
+        self.modelbestcheck=ModelCheckpoint(outputDir+"/KERAS_check_best_model.h5", 
+                                        monitor='val_loss', verbose=1, 
+                                        save_best_only=True)
+
+        self.modelbestcheckweights=ModelCheckpoint(outputDir+"/KERAS_check_best_model_weights.h5", 
+                                            monitor='val_loss', verbose=1, 
+                                            save_best_only=True,save_weights_only=True)
+
+        self.modelcheckperiod=ModelCheckpoint(outputDir+"/KERAS_check_model_epoch{epoch:02d}.h5", verbose=1,period=10)
+
+        self.modelcheck=ModelCheckpoint(outputDir+"/KERAS_check_model_last.h5", verbose=1)
+
+        self.modelcheckweights=ModelCheckpoint(outputDir+"/KERAS_check_model_last_weights.h5", verbose=1,save_weights_only=True)
+
+        self.tb = TensorBoard(log_dir=outputDir+'/logs')
+
+        self.history=History()
+        self.timer = Losstimer()
+
+        self.callbacks=[
+            self.nl_begin, 
+            self.modelbestcheck,self.modelbestcheckweights, self.modelcheck,self.modelcheckweights,self.modelcheckperiod,
+            self.reduce_lr, self.stopping, self.nl_end, self.tb, self.history,
+            self.timer
+        ]