Added colored bands and added more pens.

Models/model_sk_convnext_v1.py

@@ -11,13 +11,24 @@ import matplotlib.pyplot as plt
 # This model was trained with 13,185 images
 # See CNNImageProcessor solution for creating the test images for training this model
 # **************************************** I M P O R T A N T ********************************************************
+# There is a shell script in the Scripts folder. setup_tf_gpu.sh  Copy the script to the CNN folder and run it.  It will
+# create the venv enviroment and install python 3.10 and tensorflow (gpu)
+# to start the environment "source tf_gpu/bin/activate"
+# then type "code ."
 # Train the model on EUPORIE laptop using the GPU card with WSL2.  (Windows Subsystem For Linux).  I am running Ubuntu1 22.04.2
 # To launch WSL open up a command prompt, run powershell and type "wsl".
 # The folder structure will be /home/pi/CNN.
 # You can access the folder structure through windows explorer.  type "\\wsl$" in explorer and navigate to the folder.
 # drop in the Data and Model and run the model
-# There is a shell script in the Scripts folder. setup_tf_gpu.sh  Copy teh script to the CNN folder and run it.  It will
-# create the venv enviroment and install python 3.10 and tensorflow (gpu)
+#
+# If you want to run the model_host.py from the WSL2 environment (recommended for backtesting speed) you first need to get the address that was assigned to the 
+# WSL environment.  On the WSL inatance type "hostname -I".  Next, from the host environment you need to forward port requests
+# on port 5000 (Flask Listener Port) to the WSL environment.  Here is how to do that.  Predictions on the WSL environment
+# run considerable faster than on the host because the WSL environment is able to utilize the GPU.
+# {To Create a Port Forward} netsh interface portproxy add v4tov4 listenport=5000 listenaddress=0.0.0.0 connectport=5000 connectaddress=172.29.110.64
+# {To List Port Forwards} netsh interface portproxy show all
+# {To Remove the Port Forward}  netsh interface portproxy delete v4tov4 listenport=5000 listenaddress=0.0.0.0
+# hostname -I 
 # ********************************************************************************************************************
 
 # Figure out if we are training in CPU or GPU
@@ -39,7 +50,7 @@ tensorboard = TensorBoard(log_dir=log_dir)
 # Configuration
 # -----------------------
 shuffle_count=3000
-dataset_path = 'C:\\boneyard\\DeepLearning\\data'
+dataset_path = '/home/pi/DeepLearning/Data'
 image_size = (actualImageDimension, actualImageDimension)
 batch_size = 16    # try 16  was 32
 image_size=(actualImageDimension, actualImageDimension)
@@ -74,22 +85,7 @@ val_ds = tf.keras.preprocessing.image_dataset_from_directory(
 # -----------------------
 # Data Augmentation
 # -----------------------
-
-# data_augmentation = tf.keras.Sequential([
-#     layers.RandomFlip("horizontal"),
-#     layers.RandomRotation(0.1)
-# ])
-#data_augmentation = tf.keras.Sequential([
-#    layers.RandomFlip("horizontal"),
-#    layers.RandomRotation(0.1),  
-#    layers.RandomRotation(0.1, fill_mode="nearest"),
-#    layers.RandomZoom(0.1)
-#])
-
-
-# def preprocess_train(x, y):
-#     x = data_augmentation(x, training=True)
-#     return x, y
+# I do this in c#-land
 
 def preprocess_val(x, y):
     return x, y
@@ -106,22 +102,6 @@ train_ds = (
 )
 
 
-# for images, labels in train_ds.take(1):
-
-#     plt.figure(figsize=(10,10))
-
-#     for i in range(12):
-#         ax = plt.subplot(3,4,i+1)
-#         plt.imshow(images[i].numpy().astype("uint8"))
-#         plt.title(int(labels[i].numpy()))
-#         plt.axis("off")
-
-#     plt.tight_layout()
-#     plt.show()
-
-
-
-
 # -----------------------
 # ConvNeXt-Tiny Base Model
 # -----------------------
@@ -143,13 +123,6 @@ inputs = tf.keras.Input(shape=(actualImageDimension, actualImageDimension, 3))
 
 x = preprocess_input(inputs)
 x = base_model(x)
-# Dense Head
-# x = layers.GlobalAveragePooling2D()(x)
-# x = layers.BatchNormalization()(x)
-# x = layers.Dense(512, activation="relu")(x)
-# x = layers.Dropout(0.3)(x)
-# x = layers.Dense(128, activation="relu")(x)
-
 x = layers.GlobalAveragePooling2D()(x)
 x = layers.BatchNormalization()(x)
 x = layers.Dense(256, activation="relu")(x)
@@ -228,36 +201,3 @@ history_fine = model.fit(
     validation_data=val_ds,
     callbacks=[tensorboard, lr_scheduler, early_stopping, checkpointer]
 )
-
-
-# -----------------------
-# Plot Results
-# -----------------------
-
-def plot_history(hist, title_prefix=""):
-    plt.figure()
-    plt.plot(hist.history['accuracy'], label='Train Accuracy')
-    plt.plot(hist.history['val_accuracy'], label='Val Accuracy')
-    plt.title(f'{title_prefix} Accuracy')
-    plt.xlabel('Epochs')
-    plt.ylabel('Accuracy')
-    plt.legend()
-    plt.show()
-
-    plt.figure()
-    plt.plot(hist.history['loss'], label='Train Loss')
-    plt.plot(hist.history['val_loss'], label='Val Loss')
-    plt.title(f'{title_prefix} Loss')
-    plt.xlabel('Epochs')
-    plt.ylabel('Loss')
-    plt.legend()
-    plt.show()
-
-plot_history(history, "Initial Training")
-plot_history(history_fine, "Fine-Tuning")
-
-# -----------------------
-# Save Final Model
-# -----------------------
-
-#model.save(modelname)