Fixing alignment header issue for Gesture model file generated.

Author: kthakore

GestureToEmoji/arduino_tinyml_workshop.ipynb

@@ -26,6 +26,7 @@
         "## Gesture recognition tutorial\n",
         " * Sandeep Mistry - Arduino\n",
         " * Don Coleman - Chariot Solutions\n",
+        " * [Kartik Thakore](https://www.linkedin.com/in/kartikthakore/) (simple alignment fix)\n",
         "\n",
         " \n",
         "https://github.com/arduino/ArduinoTensorFlowLiteTutorials/"
@@ -40,7 +41,7 @@
       "source": [
         "## Setup Python Environment \n",
         "\n",
-        "The next cell sets up the dependencies in required for the notebook, run it."
+        "Install up the Python libraries and Linux tools for the code in the notebook."
       ]
     },
     {
@@ -54,7 +55,8 @@
         "# Setup environment\n",
         "!apt-get -qq install xxd\n",
         "!pip install pandas numpy matplotlib\n",
-        "!pip install tensorflow==2.0.0-rc1"
+        "%tensorflow_version 2.x\n",
+        "!pip install tensorflow"
       ],
       "execution_count": 0,
       "outputs": []
@@ -68,9 +70,9 @@
       "source": [
         "# Upload Data\n",
         "\n",
-        "1. Open the panel on the left side of Colab by clicking on the __>__\n",
-        "1. Select the files tab\n",
-        "1. Drag `punch.csv` and `flex.csv` files from your computer to the tab to upload them into colab."
+        "1. If necessary, open the panel on the left side of Colab by clicking on the __>__\n",
+        "1. Select the files tab in the left panel\n",
+        "1. Drag the `punch.csv` and `flex.csv` files from your computer to the tab to upload them into colab."
       ]
     },
     {
@@ -82,7 +84,7 @@
       "source": [
         "# Graph Data (optional)\n",
         "\n",
-        "We'll graph the input files on two separate graphs, acceleration and gyroscope, as each data set has different units and scale."
+        "Plot the CSV data on two separate graphs, acceleration and gyroscope, because each data set has different units and scale."
       ]
     },
     {
@@ -97,7 +99,7 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "\n",
-        "filename = \"punch.csv\"\n",
+        "filename = \"flex.csv\"\n",
         "\n",
         "df = pd.read_csv(\"/content/\" + filename)\n",
         "\n",
@@ -148,9 +150,9 @@
       "source": [
         "## Parse and prepare the data\n",
         "\n",
-        "The next cell parses the csv files and transforms them to a format that will be used to train the fully connected neural network.\n",
+        "Parse the CSV files and transforms them to a format that can be used to train the fully connected neural network.\n",
         "\n",
-        "Update the `GESTURES` list with the gesture data you've collected in `.csv` format.\n"
+        "If you've recorded additional gestures, update the `GESTURES` list with the names of the additional CSV files.\n"
       ]
     },
     {
@@ -174,10 +176,10 @@
         "np.random.seed(SEED)\n",
         "tf.random.set_seed(SEED)\n",
         "\n",
-        "# the list of gestures that data is available for\n",
+        "# the list of gestures \n",
         "GESTURES = [\n",
         "    \"punch\",\n",
-        "    \"flex\",\n",
+        "    \"flex\"\n",
         "]\n",
         "\n",
         "SAMPLES_PER_GESTURE = 119\n",
@@ -198,6 +200,10 @@
         "  output = ONE_HOT_ENCODED_GESTURES[gesture_index]\n",
         "  \n",
         "  df = pd.read_csv(\"/content/\" + gesture + \".csv\")\n",
+        "\n",
+        "  # get rid of pesky empty value lines of csv which cause NaN inputs to TensorFlow\n",
+        "  df = df.dropna()\n",
+        "  df = df.reset_index(drop=True)\n",
         "  \n",
         "  # calculate the number of gesture recordings in the file\n",
         "  num_recordings = int(df.shape[0] / SAMPLES_PER_GESTURE)\n",
@@ -227,6 +233,7 @@
         "inputs = np.array(inputs)\n",
         "outputs = np.array(outputs)\n",
         "\n",
+        "print(inputs.size)\n",
         "print(\"Data set parsing and preparation complete.\")"
       ],
       "execution_count": 0,
@@ -302,7 +309,8 @@
         "model = tf.keras.Sequential()\n",
         "model.add(tf.keras.layers.Dense(50, activation='relu')) # relu is used for performance\n",
         "model.add(tf.keras.layers.Dense(15, activation='relu'))\n",
-        "model.add(tf.keras.layers.Dense(NUM_GESTURES, activation='softmax')) # softmax is used, because we only expect one gesture to occur per input\n",
+        "# the final layer is softmax because we only expect one gesture to occur per input\n",
+        "model.add(tf.keras.layers.Dense(NUM_GESTURES, activation='softmax'))\n",
         "model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
         "history = model.fit(inputs_train, outputs_train, epochs=600, batch_size=1, validation_data=(inputs_validate, outputs_validate))\n",
         "\n"
@@ -438,7 +446,7 @@
       },
       "source": [
         "### Run with Test Data\n",
-        "Put our test data into the model and plot the predictions\n"
+        "Put our test data into the model and compare the predictions vs actual output\n"
       ]
     },
     {
@@ -454,14 +462,7 @@
         "\n",
         "# print the predictions and the expected ouputs\n",
         "print(\"predictions =\\n\", np.round(predictions, decimals=3))\n",
-        "print(\"actual =\\n\", outputs_test)\n",
-        "\n",
-        "# Plot the predictions along with to the test data\n",
-        "plt.clf()\n",
-        "plt.title('Training data predicted vs actual values')\n",
-        "plt.plot(inputs_test, outputs_test, 'b.', label='Actual')\n",
-        "plt.plot(inputs_test, predictions, 'r.', label='Predicted')\n",
-        "plt.show()"
+        "print(\"actual =\\n\", outputs_test)"
       ],
       "execution_count": 0,
       "outputs": []
@@ -522,7 +523,7 @@
         "colab": {}
       },
       "source": [
-        "!echo \"const unsigned char model[] = {\" > /content/model.h\n",
+        "!printf \"// We need to keep the data array aligned on some architectures.\\n#ifdef __has_attribute\\n#define HAVE_ATTRIBUTE(x) __has_attribute(x)\\n#else\\n#define HAVE_ATTRIBUTE(x) 0\\n#endif\\n#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))\\n#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4)))\\n#else\\n#define DATA_ALIGN_ATTRIBUTE\\n#endif\\n\\n const unsigned char model[] DATA_ALIGN_ATTRIBUTE = {\" > /content/model.h\n",
         "!cat gesture_model.tflite | xxd -i      >> /content/model.h\n",
         "!echo \"};\"                              >> /content/model.h\n",
         "\n",
@@ -545,6 +546,19 @@
         "\n",
         "Now it's time to switch back to the tutorial instructions and run our new model on the Arduino Nano 33 BLE Sense to classify the accelerometer and gyroscope data.\n"
       ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fsg9_6rkeGvG",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
     }
   ]
-}
+}