Lectures/Lecture27/3dasm_Lecture27.ipynb

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    "<img src=../figures/Brown_logo.svg width=50%>\n",
    "\n",
    "## Data-Driven Design & Analyses of Structures & Materials (3dasm)\n",
    "\n",
    "## Lecture 27\n",
    "\n",
    "### Miguel A. Bessa | <a href = \"mailto: miguel_bessa@brown.edu\">miguel_bessa@brown.edu</a>  | Associate Professor"
   ]
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    "**What:** A lecture of the \"3dasm\" course\n",
    "\n",
    "**Where:** This notebook comes from this [repository](https://github.com/bessagroup/3dasm_course)\n",
    "\n",
    "**Reference for entire course:** Murphy, Kevin P. *Probabilistic machine learning: an introduction*. MIT press, 2022. Available online [here](https://probml.github.io/pml-book/book1.html)\n",
    "\n",
    "**How:** We try to follow Murphy's book closely, but the sequence of Chapters and Sections is different. The intention is to use notebooks as an introduction to the topic and Murphy's book as a resource.\n",
    "* If working offline: Go through this notebook and read the book.\n",
    "* If attending class in person: listen to me (!) but also go through the notebook in your laptop at the same time. Read the book.\n",
    "* If attending lectures remotely: listen to me (!) via Zoom and (ideally) use two screens where you have the notebook open in 1 screen and you see the lectures on the other. Read the book."
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    "**Optional reference (the \"bible\" by the \"bishop\"... pun intended 😆) :** Bishop, Christopher M. *Pattern recognition and machine learning*. Springer Verlag, 2006.\n",
    "\n",
    "**References/resources to create this notebook:**\n",
    "* This simple tutorial is still based on a script I created for this article: https://imechanica.org/node/23957\n",
    "* It follows from some examples provided by the scikit-learn user guide, which seem to have originated from Mathieu Blondel, Jake Vanderplas, Vincent Dubourg, and Jan Hendrik Metzen.\n",
    "\n",
    "Apologies in advance if I missed some reference used in this notebook. Please contact me if that is the case, and I will gladly include it here."
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    "## **OPTION 1**. Run this notebook **locally in your computer**:\n",
    "1. Confirm that you have the '3dasm' mamba (or conda) environment (see Lecture 1).\n",
    "2. Go to the 3dasm_course folder in your computer and pull the last updates of the [repository](https://github.com/bessagroup/3dasm_course):\n",
    "```\n",
    "git pull\n",
    "```\n",
    "    - Note: if you can't pull the repo due to conflicts (and you can't handle these conflicts), use this command (with **caution**!) and your repo becomes the same as the one online:\n",
    "        ```\n",
    "        git reset --hard origin/main\n",
    "        ```\n",
    "3. Open command window and load jupyter notebook (it will open in your internet browser):\n",
    "```\n",
    "jupyter notebook\n",
    "```\n",
    "5. Open notebook of this Lecture and choose the '3dasm' kernel."
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    "## **OPTION 2**. Use **Google's Colab** (no installation required, but times out if idle):\n",
    "\n",
    "1. go to https://colab.research.google.com\n",
    "2. login\n",
    "3. File > Open notebook\n",
    "4. click on Github (no need to login or authorize anything)\n",
    "5. paste the git link: https://github.com/bessagroup/3dasm_course\n",
    "6. click search and then click on the notebook for this Lecture."
   ]
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    "# Basic plotting tools needed in Python.\n",
    "\n",
    "import matplotlib.pyplot as plt # import plotting tools to create figures\n",
    "import numpy as np # import numpy to handle a lot of things!\n",
    "from IPython.display import display, Math # to print with Latex math\n",
    "\n",
    "%config InlineBackend.figure_format = \"retina\" # render higher resolution images in the notebook\n",
    "plt.rcParams[\"figure.figsize\"] = (8,4) # rescale figure size appropriately for slides\n",
    "\n",
    "# We will use tensorflow for this lecture. Sometimes Tensorflow can be verbose with its warning messages.\n",
    "# We will suppress these messages with the following code:\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
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    "## Outline for today\n",
    "\n",
    "* Introduction to Artificial Neural Networks\n",
    "\n",
    "**Reading material**: This notebook + (ANNs in Chapter 13)"
   ]
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    "## Today is our first lecture on artificial neural networks\n",
    "\n",
    "We will introduce the basic idea behind **Artificial Neural Networks** (ANNs), and we will continue to refine these ideas in the next lectures."
   ]
  },
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     "text": [
      "Here's a print of X_train:\n",
      " [[6.12244898]\n",
      " [8.57142857]\n",
      " [7.14285714]\n",
      " [2.85714286]\n",
      " [8.97959184]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Function to \"learn\"\n",
    "def f(x):\n",
    "    return x * np.sin(x)\n",
    "\n",
    "n_data = 50 # number of points in our dataset\n",
    "testset_ratio = 0.90 # ratio of test set points from the dataset\n",
    "x_data = np.linspace(0, 10, n_data) # uniformly spaced points\n",
    "y_data = f(x_data) # function values at x_data\n",
    "\n",
    "X_data = np.reshape(x_data,(-1,1)) # a 2D array that scikit-learn likes\n",
    "\n",
    "seed = 1987 # set a random seed so that everyone gets the same result\n",
    "np.random.seed(seed)\n",
    "\n",
    "# Let's split into 10% training points and the rest for testing:\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data,\n",
    "                                    y_data, test_size=testset_ratio,\n",
    "                                    random_state=seed)\n",
    "\n",
    "x_train = X_train.ravel() # just for plotting later\n",
    "x_test = X_test.ravel() # just for plotting later\n",
    "\n",
    "print(\"Here's a print of X_train:\\n\", X_train)"
   ]
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      "Note that X_train and X_test are the same data that we used for the noiseless case.\n",
      "Here's a print of X_train:\n",
      " [[6.12244898]\n",
      " [8.57142857]\n",
      " [7.14285714]\n",
      " [2.85714286]\n",
      " [8.97959184]]\n"
     ]
    }
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   "source": [
    "# Now let's also create the noisy dataset:\n",
    "random_std = 0.5 + 1.0 * np.random.random(y_data.shape) # np.random.random returns random number between [0.0, 1.0)\n",
    "noise = np.random.normal(0, random_std) # sample vector from Gaussians with random standard deviation\n",
    "y_noisy_data = y_data + noise # Perturb every y_data point with Gaussian noise\n",
    "\n",
    "# Pair up points with their associated noise level (because of train_test_split):\n",
    "Y_noisy_data = np.column_stack((y_noisy_data,noise))\n",
    "\n",
    "# Split into 10% training points and the rest for testing:\n",
    "X_train, X_test, Y_noisy_train, Y_noisy_test = train_test_split(X_data,\n",
    "                                    Y_noisy_data, test_size=testset_ratio,\n",
    "                                    random_state=seed) # \"noisy_train\" is a great name for a variable, hein?\n",
    "# NOTE: since we are using the same seed and we do train_test_split on the same X_data and since y_noisy_data\n",
    "#       is just y_data + noise, we are splitting the dataset exactly in the same way! This is nice because we\n",
    "#       want to keep the comparison as fair as possible.\n",
    "\n",
    "# Finally, for plotting purposes, let's convert the 2D arrays into 1D arrays (vectors):\n",
    "x_train = X_train.ravel()\n",
    "x_test = X_test.ravel()\n",
    "y_noisy_train = Y_noisy_train[:,0]\n",
    "noise_train = Y_noisy_train[:,1]\n",
    "y_noisy_test = Y_noisy_test[:,0]\n",
    "noise_test = Y_noisy_test[:,1]\n",
    "\n",
    "print(\"Note that X_train and X_test are the same data that we used for the noiseless case.\")\n",
    "print(\"Here's a print of X_train:\\n\", X_train)"
   ]
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   "source": [
    "## Short tutorial on 1D regression with Artificial Neural Networks\n",
    "\n",
    "ANNs for regression are not that different when compared to linear regression."
   ]
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    "Consider again the observation distribution as we wrote it in the beginning of the course:\n",
    "\n",
    "$$\n",
    "p(y|x, \\mathbf{z}) = \\mathcal{N}(y| \\mu_{y|z} = f(x;\\, \\mathbf{z}), \\sigma_{y|z}^2 = \\sigma^2)\n",
    "$$"
   ]
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   "source": [
    "But now, instead of modeling the mean of the distribution using polynomial basis functions by $f(x;\\, \\mathbf{z}) = \\mathbf{w}^T \\boldsymbol{\\phi}(x)$, consider a simple extension where the **basis functions also have their own parameters**:\n",
    "\n",
    "$$\n",
    "f(x;\\, \\mathbf{z}) = \\mathbf{w}^T \\boldsymbol{\\phi}(x;\\, \\mathbf{z}_2)\n",
    "$$"
   ]
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   "source": [
    "where $\\mathbf{z} = (\\mathbf{z}_1,\\, \\mathbf{z}_2)$ and $\\mathbf{z}_1 = (\\mathbf{w},\\, \\mathbf{b})$ are all the parameters of the model."
   ]
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   "source": [
    "Instead of stopping here,\n",
    "\n",
    "$$\n",
    "f(x;\\, \\mathbf{z}) = \\mathbf{w}^T \\boldsymbol{\\phi}(x;\\, \\mathbf{z}_2)\n",
    "$$\n",
    "\n",
    "we can also repeat this recursively $L$ times to create more complex transformations:\n",
    "\n",
    "$$\n",
    "f(x;\\, \\mathbf{z}) = f_L\\left( f_{L-1}\\left(\\cdots \\left(f_1\\left(x\\right)\\right)\\cdots\\right)\\right)\n",
    "$$\n",
    "\n",
    "where $f_l(x) = f(x;\\, \\mathbf{z}_l)$ is the function at layer $l$ of our recursive transformation.\n",
    "\n",
    "This is the key idea behind **deep neural networks**.\n",
    "\n",
    "* Confused?"
   ]
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   "source": [
    "Let's try to see this for a very simple case. Consider again the observation distribution:\n",
    "\n",
    "$$\n",
    "p(y|x, \\mathbf{z}) = \\mathcal{N}(y| \\mu_{y|z} = f(x;\\, \\mathbf{z}), \\sigma_{y|z}^2 = \\sigma^2)\n",
    "$$\n",
    "\n",
    "The simplest extension comes from using a nonlinear function $f_1$ acting on our well-known linear model:\n",
    "\n",
    "$$\n",
    "\\mu_{y|z} = f(x;\\, \\mathbf{z}) = f_1\\left( \\mathbf{w}^T \\boldsymbol{\\phi}(x) \\right) = f_1\\left( f_0 \\left(x; \\mathbf{z}_0 \\right) \\right) = f_1\\left(b_0 + w_0 x\\right)\n",
    "$$\n",
    "\n",
    "where we are calling our linear regression model as $f_0 \\left(x; \\mathbf{z}_0 \\right)$."
   ]
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  {
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   "source": [
    "So, our model for the mean of the observation distribution becomes:\n",
    "\n",
    "$$\n",
    "\\mu_{y|z} = f_1\\left( b_0 + w_0 x \\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Does this remind you of a model we discussed before?"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "* If $f_1$ is the sigmoid function, then this is what we used in Logistic Regression!\n",
    "    * Of course, in that case, the observation distribution was the Bernoulli distribution (instead of the Gaussian distribution) because it was a classification problem, and the mean was the $\\rho$ parameter. "
   ]
  },
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   "source": [
    "Anyway, back to our model for the mean of our Gaussian observation distribution:\n",
    "\n",
    "$$\n",
    "\\mu_{y|z} = f_1\\left( b_0 + w_0 x \\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "fragment"
    }
   },
   "source": [
    "Why stop here?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "fragment"
    }
   },
   "source": [
    "Why not introducing more parameters?\n",
    "\n",
    "$$\n",
    "\\mu_{y|z} = b_1 + w_1 f_1\\left( b_0 + w_0 x \\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "But why stop here?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "fragment"
    }
   },
   "source": [
    "What if we apply another nonlinear function $f_2$?\n",
    "\n",
    "$$\n",
    "\\mu_{y|z} = f_2\\left(b_1 + w_1 f_1\\left( b_0 + w_0 x \\right)\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "fragment"
    }
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   "source": [
    "But why stop here?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "What about doing this recursively $L$ times to create more complex transformations?\n",
    "\n",
    "$$\n",
    "f(x;\\, \\mathbf{z}) = f_L\\left( f_{L-1}\\left(\\cdots \\left(f_1\\left(b_0 + w_0 x\\right)\\right)\\cdots\\right)\\right)\n",
    "$$\n",
    "\n",
    "This is one of the simplest examples of an Artificial Neural Network architecture called feedforward architecture."
   ]
  },
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   "source": [
    "We will look into this model carefully in the next lectures.\n",
    "\n",
    "For now, I just want to draw a schematic so that you understand the number of parameters that starts appearing!\n",
    "\n",
    "* Draw on the board a feedforward ANN with 2 hidden layers for 1D case.\n",
    "    * First hidden layer with 3 neurons and second hidden layer with 2 neurons."
   ]
  },
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   "source": [
    "Two examples of nonlinear activation functions $f_l$ for a layer $l$.\n",
    "\n",
    "\n",
    "<div>\n",
    "<img style=\"float: left\"; src=../figures/nonlin_fn.png width=500px></div>"
   ]
  },
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   "source": [
    "Let's now focus on how to create an ANN model for 1D regression using:\n",
    "1. A feedforward architecture with 2 dense hidden layers\n",
    "2. The ReLu activation function\n",
    "3. Adam optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "# Let's create a function defining our Artificial Neural Network.\n",
    "from tensorflow import keras # fast library for ANNs\n",
    "from tensorflow.keras.optimizers import Adam # import the optimizer you want to use to calculate the parameters\n",
    "from keras.models import Sequential # to create a feedforward neural network\n",
    "from keras.layers import Dense # to create a feedforward neural network with dense layers\n",
    "#\n",
    "# Function to create the ANN model (in this case we are creating )\n",
    "def create_ANN(input_dimensions=1, # number of input variables\n",
    "               neurons1=3, # number of neurons in first hidden layer\n",
    "               neurons2=2, # number of neurons in second hidden layer\n",
    "               activation='relu', # activation function\n",
    "               optimizer='adam'): # optimization algorithm to compute the weights and biases\n",
    "    # create model\n",
    "    model = Sequential() # Feedforward architecture\n",
    "    model.add(keras.Input(shape=(input_dimensions,))) # Define input layer with the number of input variables (1D)\n",
    "    model.add(Dense(neurons1, activation=activation)) # first hidden layer\n",
    "    #model.add(Dense(neurons1, input_dim=input_dimensions, activation=activation)) \n",
    "    model.add(Dense(neurons2, activation=activation)) # second hidden layer\n",
    "    model.add(Dense(1)) # output layer with just one neuron because we have only one output (1D problem!)\n",
    "    model.compile(loss='mse', # error metric to measure our NLL (loss)\n",
    "                  optimizer=optimizer)\n",
    "    return model"
   ]
  },
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   "source": [
    "In addition, let's introduce something important: dataset preprocessing.\n",
    "\n",
    "Standardizing our dataset is good practice and can be important for many ML algorithms (ANNs included)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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   "source": [
    "# Standardizing your dataset is good practice and can be important for ANNs!\n",
    "from sklearn.preprocessing import StandardScaler # standardize the dataset with scikit-learn\n",
    "#\n",
    "scaler = StandardScaler().fit(X_train) # Check scikit-learn to see what this does!\n",
    "#\n",
    "X_train_scaled=scaler.transform(X_train)\n",
    "X_test_scaled=scaler.transform(X_test)\n",
    "X_data_scaled=scaler.transform(X_data)\n",
    "#"
   ]
  },
  {
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   "execution_count": 12,
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   "source": [
    "import scikeras\n",
    "from scikeras.wrappers import KerasRegressor\n",
    "#from tensorflow.keras import KerasRegressor # a new version will use scikeras\n",
    "# Now create your first ANN model!\n",
    "neurons1=200 # number of neurons for the first hidden layer\n",
    "neurons2=10 # number of neurons for the second hidden layer\n",
    "batch_size = len(X_train) # considering the entire dataset for updating the weights and biases in each epoch\n",
    "epochs = 150 # number of times we train the neural network with the entire training set\n",
    "optimizer = Adam(learning_rate=0.001) # specifying the learning rate value for the optimizer (PLAY WITH THIS!)\n",
    "ANN_model = KerasRegressor(model=create_ANN, neurons1=neurons1, neurons2=neurons2,\n",
    "                           activation='relu',\n",
    "                           batch_size=batch_size, epochs=epochs, optimizer=optimizer)"
   ]
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     "text": [
      "Epoch 1/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 905ms/step - loss: 17.5024 - val_loss: 13.9317\n",
      "Epoch 2/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 17.3230 - val_loss: 13.8935\n",
      "Epoch 3/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 17.1470 - val_loss: 13.8604\n",
      "Epoch 4/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 16.9792 - val_loss: 13.8340\n",
      "Epoch 5/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 16.8253 - val_loss: 13.8093\n",
      "Epoch 6/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 16.6789 - val_loss: 13.7854\n",
      "Epoch 7/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 16.5341 - val_loss: 13.7625\n",
      "Epoch 8/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 16.3926 - val_loss: 13.7405\n",
      "Epoch 9/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 16.2531 - val_loss: 13.7206\n",
      "Epoch 10/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 16.1138 - val_loss: 13.6996\n",
      "Epoch 11/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 15.9745 - val_loss: 13.6782\n",
      "Epoch 12/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 15.8358 - val_loss: 13.6579\n",
      "Epoch 13/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 15.6957 - val_loss: 13.6366\n",
      "Epoch 14/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 15.5553 - val_loss: 13.6144\n",
      "Epoch 15/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 15.4135 - val_loss: 13.5936\n",
      "Epoch 16/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 15.2707 - val_loss: 13.5746\n",
      "Epoch 17/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 15.1264 - val_loss: 13.5564\n",
      "Epoch 18/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 14.9824 - val_loss: 13.5393\n",
      "Epoch 19/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 14.8370 - val_loss: 13.5232\n",
      "Epoch 20/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 14.6914 - val_loss: 13.5073\n",
      "Epoch 21/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 14.5446 - val_loss: 13.4901\n",
      "Epoch 22/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 14.3974 - val_loss: 13.4718\n",
      "Epoch 23/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 14.2480 - val_loss: 13.4522\n",
      "Epoch 24/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 14.0973 - val_loss: 13.4316\n",
      "Epoch 25/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 13.9462 - val_loss: 13.4101\n",
      "Epoch 26/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 13.7924 - val_loss: 13.3884\n",
      "Epoch 27/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 13.6372 - val_loss: 13.3672\n",
      "Epoch 28/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 13.4809 - val_loss: 13.3461\n",
      "Epoch 29/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 13.3228 - val_loss: 13.3258\n",
      "Epoch 30/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 13.1636 - val_loss: 13.3069\n",
      "Epoch 31/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 13.0010 - val_loss: 13.2891\n",
      "Epoch 32/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 12.8371 - val_loss: 13.2718\n",
      "Epoch 33/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 12.6707 - val_loss: 13.2550\n",
      "Epoch 34/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 12.5021 - val_loss: 13.2381\n",
      "Epoch 35/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 12.3308 - val_loss: 13.2212\n",
      "Epoch 36/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 12.1576 - val_loss: 13.2037\n",
      "Epoch 37/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 11.9817 - val_loss: 13.1856\n",
      "Epoch 38/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 11.8039 - val_loss: 13.1671\n",
      "Epoch 39/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 11.6235 - val_loss: 13.1476\n",
      "Epoch 40/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 11.4407 - val_loss: 13.1284\n",
      "Epoch 41/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 11.2558 - val_loss: 13.1096\n",
      "Epoch 42/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 11.0693 - val_loss: 13.0912\n",
      "Epoch 43/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 10.8808 - val_loss: 13.0734\n",
      "Epoch 44/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 10.6898 - val_loss: 13.0563\n",
      "Epoch 45/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 10.4967 - val_loss: 13.0397\n",
      "Epoch 46/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 10.3022 - val_loss: 13.0235\n",
      "Epoch 47/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 10.1060 - val_loss: 13.0074\n",
      "Epoch 48/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 9.9084 - val_loss: 12.9921\n",
      "Epoch 49/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 9.7101 - val_loss: 12.9779\n",
      "Epoch 50/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 9.5106 - val_loss: 12.9652\n",
      "Epoch 51/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 9.3100 - val_loss: 12.9535\n",
      "Epoch 52/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 9.1081 - val_loss: 12.9426\n",
      "Epoch 53/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 8.9063 - val_loss: 12.9323\n",
      "Epoch 54/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.7039 - val_loss: 12.9229\n",
      "Epoch 55/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.5007 - val_loss: 12.9144\n",
      "Epoch 56/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 8.2976 - val_loss: 12.9069\n",
      "Epoch 57/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.0941 - val_loss: 12.9000\n",
      "Epoch 58/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 7.8904 - val_loss: 12.8942\n",
      "Epoch 59/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 7.6878 - val_loss: 12.8908\n",
      "Epoch 60/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.4841 - val_loss: 12.8893\n",
      "Epoch 61/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.2823 - val_loss: 12.8899\n",
      "Epoch 62/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 7.0812 - val_loss: 12.8930\n",
      "Epoch 63/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.8803 - val_loss: 12.8977\n",
      "Epoch 64/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 6.6812 - val_loss: 12.9047\n",
      "Epoch 65/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 6.4838 - val_loss: 12.9135\n",
      "Epoch 66/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.2895 - val_loss: 12.9252\n",
      "Epoch 67/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 6.0964 - val_loss: 12.9395\n",
      "Epoch 68/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 5.9062 - val_loss: 12.9568\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 69/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.7194 - val_loss: 12.9777\n",
      "Epoch 70/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 5.5361 - val_loss: 13.0016\n",
      "Epoch 71/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 5.3569 - val_loss: 13.0298\n",
      "Epoch 72/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 5.1812 - val_loss: 13.0617\n",
      "Epoch 73/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 5.0106 - val_loss: 13.0975\n",
      "Epoch 74/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 4.8447 - val_loss: 13.1376\n",
      "Epoch 75/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 4.6844 - val_loss: 13.1811\n",
      "Epoch 76/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 4.5295 - val_loss: 13.2288\n",
      "Epoch 77/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 4.3797 - val_loss: 13.2791\n",
      "Epoch 78/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 4.2359 - val_loss: 13.3320\n",
      "Epoch 79/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 4.0974 - val_loss: 13.3879\n",
      "Epoch 80/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 3.9657 - val_loss: 13.4475\n",
      "Epoch 81/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 3.8404 - val_loss: 13.5106\n",
      "Epoch 82/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 3.7212 - val_loss: 13.5766\n",
      "Epoch 83/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 3.6089 - val_loss: 13.6460\n",
      "Epoch 84/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 3.5032 - val_loss: 13.7183\n",
      "Epoch 85/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 3.4036 - val_loss: 13.7933\n",
      "Epoch 86/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 3.3106 - val_loss: 13.8699\n",
      "Epoch 87/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 3.2243 - val_loss: 13.9480\n",
      "Epoch 88/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 3.1440 - val_loss: 14.0262\n",
      "Epoch 89/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 3.0705 - val_loss: 14.1033\n",
      "Epoch 90/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 3.0027 - val_loss: 14.1819\n",
      "Epoch 91/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.9408 - val_loss: 14.2616\n",
      "Epoch 92/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 2.8842 - val_loss: 14.3411\n",
      "Epoch 93/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.8340 - val_loss: 14.4193\n",
      "Epoch 94/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.7893 - val_loss: 14.4972\n",
      "Epoch 95/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.7483 - val_loss: 14.5737\n",
      "Epoch 96/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.7113 - val_loss: 14.6483\n",
      "Epoch 97/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.6794 - val_loss: 14.7204\n",
      "Epoch 98/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.6502 - val_loss: 14.7905\n",
      "Epoch 99/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 2.6250 - val_loss: 14.8558\n",
      "Epoch 100/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.6031 - val_loss: 14.9178\n",
      "Epoch 101/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 2.5834 - val_loss: 14.9747\n",
      "Epoch 102/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.5654 - val_loss: 15.0275\n",
      "Epoch 103/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.5494 - val_loss: 15.0769\n",
      "Epoch 104/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 2.5351 - val_loss: 15.1212\n",
      "Epoch 105/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.5228 - val_loss: 15.1615\n",
      "Epoch 106/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.5123 - val_loss: 15.1954\n",
      "Epoch 107/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.5022 - val_loss: 15.2263\n",
      "Epoch 108/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.4927 - val_loss: 15.2544\n",
      "Epoch 109/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 2.4839 - val_loss: 15.2781\n",
      "Epoch 110/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 2.4762 - val_loss: 15.2973\n",
      "Epoch 111/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.4675 - val_loss: 15.3120\n",
      "Epoch 112/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 2.4585 - val_loss: 15.3205\n",
      "Epoch 113/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 2.4494 - val_loss: 15.3231\n",
      "Epoch 114/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.4400 - val_loss: 15.3193\n",
      "Epoch 115/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.4304 - val_loss: 15.3128\n",
      "Epoch 116/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.4210 - val_loss: 15.3040\n",
      "Epoch 117/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 2.4110 - val_loss: 15.2931\n",
      "Epoch 118/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.4000 - val_loss: 15.2787\n",
      "Epoch 119/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.3899 - val_loss: 15.2610\n",
      "Epoch 120/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 2.3786 - val_loss: 15.2403\n",
      "Epoch 121/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 2.3675 - val_loss: 15.2173\n",
      "Epoch 122/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.3569 - val_loss: 15.1902\n",
      "Epoch 123/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.3454 - val_loss: 15.1613\n",
      "Epoch 124/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.3345 - val_loss: 15.1306\n",
      "Epoch 125/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.3228 - val_loss: 15.0981\n",
      "Epoch 126/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 2.3115 - val_loss: 15.0657\n",
      "Epoch 127/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 2.3002 - val_loss: 15.0325\n",
      "Epoch 128/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 2.2890 - val_loss: 14.9982\n",
      "Epoch 129/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 2.2777 - val_loss: 14.9639\n",
      "Epoch 130/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.2661 - val_loss: 14.9293\n",
      "Epoch 131/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 2.2554 - val_loss: 14.8931\n",
      "Epoch 132/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.2439 - val_loss: 14.8550\n",
      "Epoch 133/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.2318 - val_loss: 14.8185\n",
      "Epoch 134/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.2212 - val_loss: 14.7838\n",
      "Epoch 135/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.2093 - val_loss: 14.7505\n",
      "Epoch 136/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.1982 - val_loss: 14.7199\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 137/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 2.1875 - val_loss: 14.6898\n",
      "Epoch 138/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.1765 - val_loss: 14.6633\n",
      "Epoch 139/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.1659 - val_loss: 14.6358\n",
      "Epoch 140/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.1545 - val_loss: 14.6066\n",
      "Epoch 141/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.1428 - val_loss: 14.5776\n",
      "Epoch 142/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.1324 - val_loss: 14.5475\n",
      "Epoch 143/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 2.1207 - val_loss: 14.5167\n",
      "Epoch 144/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.1098 - val_loss: 14.4895\n",
      "Epoch 145/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.0984 - val_loss: 14.4638\n",
      "Epoch 146/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 2.0881 - val_loss: 14.4381\n",
      "Epoch 147/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.0773 - val_loss: 14.4088\n",
      "Epoch 148/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 2.0664 - val_loss: 14.3776\n",
      "Epoch 149/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.0558 - val_loss: 14.3486\n",
      "Epoch 150/150\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 2.0452 - val_loss: 14.3203\n"
     ]
    }
   ],
   "source": [
    "# Now that we created our first ANN model, let's fit it to our (scaled) dataset!\n",
    "history = ANN_model.fit(X_train_scaled, y_train, validation_data=(X_test_scaled, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 510ms/step\n"
     ]
    }
   ],
   "source": [
    "fig_ANN, (ax1_ANN, ax2_ANN) = plt.subplots(1,2)\n",
    "# Create a plot for the loss history\n",
    "ax1_ANN.plot(history.history_['loss']) # plot training loss\n",
    "ax1_ANN.plot(history.history_['val_loss']) # plot testing loss\n",
    "ax1_ANN.set_title('Training and testing loss', fontsize=20)\n",
    "ax1_ANN.set_ylabel('loss', fontsize=20)\n",
    "ax1_ANN.set_xlabel('epoch', fontsize=20)\n",
    "ax1_ANN.legend(['training', 'testing'], loc='upper right', fontsize=15)\n",
    "\n",
    "# Create a plot for the ANN prediction\n",
    "ax2_ANN.plot(x_data, f(x_data), 'r:', label=u'ground truth: $f(x) = x\\,\\sin(x)$') # show ground truth function\n",
    "ax2_ANN.plot(x_train, y_train, 'ro', markersize=6, label=\"training points\") # show training data\n",
    "ax2_ANN.plot(x_test, y_test, 'kX', markersize=6, label=\"testing points\") # show testing data\n",
    "\n",
    "y_pred = history.model_.predict(X_data_scaled) # predict all data points with ANN\n",
    "\n",
    "ax2_ANN.plot(x_data, y_pred, 'b-', label=\"Neural Network prediction\") # plot prediction\n",
    "ax2_ANN.set_title(r'NN with '+str(neurons1)+' neurons in the 1st hidden layer, and '+str(neurons2)+' in the 2nd',\n",
    "                 fontsize=20)\n",
    "ax2_ANN.set_xlabel('$x$', fontsize=20)\n",
    "ax2_ANN.set_ylabel('$f(x)$', fontsize=20)\n",
    "ax2_ANN.legend(loc='upper left', fontsize=15)\n",
    "\n",
    "# Create figure with specified size\n",
    "fig_ANN.set_size_inches(16, 8)\n",
    "plt.close(fig_ANN) # do not plot the figure now. We will show it in the next cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "image/png": {
       "height": 721,
       "width": 1477
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_ANN # show figure now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Not the most amazing model you have ever seen, right?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Try again but now using 200 neurons for the first hidden layer and 10 for the second hidden layer.\n",
    "    - spoiler alert: a bit better, but far from amazing..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### See you next class\n",
    "\n",
    "Have fun!"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "hide_code_all_hidden": false,
  "kernelspec": {
   "display_name": "Python [conda env:3dasm]",
   "language": "python",
   "name": "conda-env-3dasm-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}