initial trainer

CustomCallback.py

@@ -0,0 +1,111 @@
+from streamlit.proto.Progress_pb2 import Progress
+from tensorflow import keras
+import streamlit as st
+import pandas as pd
+
+
+class CustomCallback(keras.callbacks.Callback):
+    percent_complete = 0
+    data_frame = pd.DataFrame()
+
+    def __init__(self, element, pregress, metrics):
+      self.element = element
+      self.progress = st.progress(0)
+      self.metrics = st.line_chart(self.data_frame)
+
+    def on_train_begin(self, logs=None):
+        keys = list(logs.keys())
+        keystring = "Starting training"
+        self.element.markdown(" %s*..." % keystring) 
+        #st.write(keystring)
+        #print("Starting training; got log keys: {}".format(keys))
+
+    def on_train_end(self, logs=None):
+        keys = list(logs.keys())
+        keystring = "Finished training"
+        self.progress.progress(100)
+        self.element.markdown("* %s*..." % keystring) 
+        #st.write(keystring)
+
+    def on_epoch_begin(self, epoch, logs=None):
+        keys = list(logs.keys())
+        keystring = "Start epoch {} of training"
+        self.element.markdown("* %s*..." % keystring)
+        self.data_frame = pd.DataFrame()
+        self.percent_complete = 0
+        #st.write(keystring)
+
+    def on_epoch_end(self, epoch, logs=None):
+        keys = list(logs.keys())
+        keystring = "End epoch {} of training ".format(epoch)
+        self.element.markdown("* %s*..." % keystring) 
+        #st.write()
+
+    def on_test_begin(self, logs=None):
+        keys = list(logs.keys())
+        keystring = "Start testing"
+        self.element.markdown("* %s*..." % keystring) 
+        #st.write(keystring)
+
+    def on_test_end(self, logs=None):
+        keys = list(logs.keys())
+        keystring = "Stop testing"
+        self.element.markdown("* %s*..." % keystring)
+        #st.write(keystring)
+
+    def on_predict_begin(self, logs=None):
+        keys = list(logs.keys())
+        keystring = "Start predicting"
+        #self.element.markdown("*Training %s*..." % keystring) 
+        #st.write()
+
+    def on_predict_end(self, logs=None):
+        keys = list(logs.keys())
+        keystring ="Stop predicting; got log keys"
+        #self.element.markdown("*Training %s*..." % keystring) 
+        #st.write(keystring)
+
+    def on_train_batch_begin(self, batch, logs=None):
+        keys = list(logs.keys())
+        keystring = "...Training: start of batch {}".format(batch)
+        if self.percent_complete == 100:
+            self.percent_complete = self.percent_complete - 20
+            return
+        self.percent_complete = self.percent_complete + 1
+        #st.write(self.percent_complete)
+        self.progress.progress(self.percent_complete)
+        self.element.markdown("* %s*..." % keystring) 
+        #st.write(keystring)
+
+    def on_train_batch_end(self, batch, logs=None):
+        keys = list(logs.keys())
+        keystring = "...Training: end of batch {}".format(batch)
+        #st.write(logs)
+        self.data_frame = self.data_frame.append({'loss':logs['loss'], 'accuracy':logs['accuracy']}, ignore_index=True)
+        self.metrics.line_chart(self.data_frame)
+        self.element.markdown("* %s*..." % keystring) 
+        #st.write(keystring)
+
+    def on_test_batch_begin(self, batch, logs=None):
+        keys = list(logs.keys())
+        keystring = "...Evaluating: start of batch {}".format(batch)
+        self.element.markdown("* %s*..." % keystring) 
+        #st.write()
+
+    def on_test_batch_end(self, batch, logs=None):
+        keys = list(logs.keys())
+        keystring = "...Evaluating: end of batch {}".format(batch)
+        self.element.markdown("* %s*..." % keystring) 
+        #st.write()
+
+    def on_predict_batch_begin(self, batch, logs=None):
+        keys = list(logs.keys())
+        keystring = "...Predicting: start of batch {}".format(batch)
+        #self.element.markdown("*Training %s*..." % keystring) 
+        #st.write()
+
+    def on_predict_batch_end(self, batch, logs=None):
+        keys = list(logs.keys())
+        keystring = "...Predicting: end of batch {}".format(batch)
+        #self.element.markdown("*Training %s*..." % keystring) 
+        #st.write(keystring)