Fix Mnist example running in two browsers at the same time (streamlit#216)

* update mnist demo to use TF 2.0 api
* upgrade tensorflow to 2.0.0

Author: jrhone

examples/mnist-cnn.py

@@ -24,27 +24,27 @@
 import streamlit as st
 from streamlit import config
 
-from keras.datasets import mnist
-from keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten
-from keras.models import Sequential
-from keras.optimizers import SGD
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.optimizers import SGD
 from keras.utils import np_utils
-import keras
+from tensorflow import keras
 import math
 import numpy as np
 import pandas as pd
 import time
 
-# https://kobkrit.com/using-allow-growth-memory-option-in-tensorflow-and-keras-dc8c8081bc96
-from keras.backend.tensorflow_backend import set_session
 import tensorflow as tf
 
-tf_config = tf.ConfigProto()
 # dynamically grow the memory used on the GPU
 # this option is fine on non gpus as well.
+tf_config = tf.compat.v1.ConfigProto()
 tf_config.gpu_options.allow_growth = True
 tf_config.log_device_placement = True
-set_session(tf.Session(config=tf_config))
+
+# https://kobkrit.com/using-allow-growth-memory-option-in-tensorflow-and-keras-dc8c8081bc96
+tf.compat.v1.keras.backend.set_session(tf.compat.v1.Session(config=tf_config))
 
 
 class MyCallback(keras.callbacks.Callback):
@@ -67,10 +67,12 @@ def on_epoch_begin(self, epoch, logs=None):
 
     def on_batch_end(self, batch, logs=None):
         rows = pd.DataFrame(
-            [[logs["loss"], logs["accuracy"]]], columns=["loss", "accuracy"])
+            [[logs["loss"], logs["accuracy"]]], columns=["loss", "accuracy"]
+        )
         if batch % 10 == 0:
-            self._epoch_chart.add_rows({"loss": [logs["loss"]],
-                                        "accuracy": [logs["accuracy"]]})
+            self._epoch_chart.add_rows(
+                {"loss": [logs["loss"]], "accuracy": [logs["accuracy"]]}
+            )
         if batch % 100 == 99:
             self._summary_chart.add_rows(rows)
         percent_complete = logs["batch"] * logs["size"] / self.params["samples"]
@@ -96,6 +98,7 @@ def on_epoch_end(self, epoch, logs=None):
             % {"epoch": epoch, "summary": summary}
         )
 
+
 st.title("MNIST CNN")
 
 (x_train, y_train), (x_test, y_test) = mnist.load_data()
@@ -134,8 +137,7 @@ def on_epoch_end(self, epoch, logs=None):
 model.add(Dense(8, activation="relu"))
 model.add(Dense(num_classes, activation="softmax"))
 
-model.compile(
-    loss="categorical_crossentropy", optimizer=sgd, metrics=["accuracy"])
+model.compile(loss="categorical_crossentropy", optimizer=sgd, metrics=["accuracy"])
 
 show_terminal_output = not config.get_option("server.liveSave")
 model.fit(

lib/Pipfile

@@ -25,7 +25,7 @@ bokeh = "*"
 graphviz = "*"
 parameterized = "*"
 pydot = "*"
-tensorflow = "*"
+tensorflow = ">=2.0.0"
 seaborn = "*"
 prometheus-client = "*"
 opencv-python = "*"