update slides and scripts tensorflow

docs/udocker/udocker-04.md

@@ -58,14 +58,22 @@ ssh into your favorite HPC system's head node (user interface host), install udo
 mkdir udocker-tutorial
 cd udocker-tutorial
 export UDOCKER_DIR=$HOME/udocker-tutorial/.udocker
+udocker version
 ```
 
-I assume that the compute/worker nodes mount your $HOME directory or, you can do this in some directory mounted in the compute/worker nodes.
+Check that the `UDOCKER_DIR=$HOME/udocker-tutorial/.udocker` was created
+
+```bash
+echo $UDOCKER_DIR
+ls -al $UDOCKER_DIR
+```
 
 ---
 
 ## In the beginning - II
 
+I assume that the compute/worker nodes mount your $HOME directory or, you can do this in some directory mounted in the compute/worker nodes.
+
 Git pull the repository to get needed input files, in particular for the tensorflow/keras application:
 
 ```bash

udocker-files/run-keras.sh

@@ -14,4 +14,4 @@ source udockervenv/bin/activate
 export UDOCKER_DIR=$TUT_DIR/.udocker
 
 echo "###############################"
-udocker run -v $TUT_DIR/tensorflow:/home/user -w /home/user tf_gpu python3 keras_example_small.py
+udocker run -v $TUT_DIR/tensorflow:/home/user -w /home/user tf_gpu python3 keras_2_small.py

udocker-files/tensorflow/keras_2_small.py

@@ -0,0 +1,20 @@
+import tensorflow as tf
+
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+model = tf.keras.models.Sequential([
+  tf.keras.layers.Flatten(input_shape=(28, 28)),
+  tf.keras.layers.Dense(128, activation='relu'),
+  tf.keras.layers.Dropout(0.2),
+  tf.keras.layers.Dense(10, activation='softmax')
+])
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(x_train, y_train, epochs=5)
+model.evaluate(x_test,  y_test, verbose=2)