ML-Kurs-SS2023/notebooks/03_ml_basics_minimizer_methods.ipynb

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   "source": [
    "#\n",
    "# An example of the minimzer usage in tensor flow\n",
    "# the loss function is plotted and the result in terms of a line\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "270932f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77cd99a8",
   "metadata": {},
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    "# Define the training data\n",
    "train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n",
    "                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n",
    "train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n",
    "                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n"
   ]
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    "The input to the model is represented by the train_X \n",
    "Y_train represents the target or the truth values for the training data\n",
    "The model will recieve train_X and make predictions on the weights\n",
    "The difference between these predictions and the actual target values\n",
    "train_Y will be used to update the weights and minimize the loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed8449c3",
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   "source": [
    "# Define the model to a simple linear regression with only one dense layer and\n",
    "# no activation function for the first layer all train_X points are input\n",
    "\n",
    "# model = tf.keras.models.Sequential([\n",
    "#  tf.keras.layers.Dense(1, input_shape=[1])\n",
    "#])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71e072b4",
   "metadata": {},
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   "source": [
    "# This model has 2 dense layers the first with relu activation\n",
    "# and the 2nd layer has 1 output unit and uses the default\n",
    "# linear activation function.\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Dense(17, activation='relu',input_shape=[1]),\n",
    "  tf.keras.layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fabf184",
   "metadata": {},
   "outputs": [],
   "source": [
    "# different optimizer methods can be enabled\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.01), loss='mean_squared_error')\n",
    "#model.compile(optimizer=tf.keras.optimizers.SGD(0.01), loss='mean_squared_error')\n",
    "#model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.01), loss='mean_squared_error')\n",
    "#model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.01), loss='mean_squared_error')\n",
    "#model.compile(optimizer=tf.keras.optimizers.Ftrl(learning_rate=0.015), loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22c4124f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model and access training parameters\n",
    "history = model.fit(train_X, train_Y, epochs=60)\n",
    "print(history.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46615960",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the weights of the Dense layer\n",
    "weights = model.layers[0].get_weights()\n",
    "# Print the weight matrix and bias vector\n",
    "print('Weight matrix shape:', weights[0].shape)\n",
    "print('Bias vector shape:', weights[1].shape)\n",
    "print (weights[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da12fc5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the loss function\n",
    "plt.plot(history.history['loss'])\n",
    "plt.title(\"Loss Function\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.show()\n",
    "\n",
    "# Plot the input data and the predicted values\n",
    "plt.plot(train_X, train_Y, 'ro', label=\"Original Data\")\n",
    "plt.plot(train_X, model.predict(train_X), label=\"Predicted\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60417d5f",
   "metadata": {},
   "outputs": [],
   "source": []
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