insights-dnn-diagnostics-activations-gradients.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/patimejia/dnn-diagnostics-activations-gradients?scriptVersionId=138079448\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"d9c633db","metadata":{"papermill":{"duration":0.006871,"end_time":"2023-07-27T13:36:27.689864","exception":false,"start_time":"2023-07-27T13:36:27.682993","status":"completed"},"tags":[]},"source":["---\n","\n","> #### **Goal**: This code is designed for analysis of deep neural networks. Through visualizing activations, weights, and gradients, we identify issues stemming from activation functions. Much like the length of a pendulum determines its swing frequency, (i.e. $\\frac{1}{t_{j-i}}$ hertz), activation functions shape a network's reaction to data.\n","\n","---\n","# Deep Neural Networks: Addressing the Gradient Problem\n","\n","Deep neural networks are powerful tools in machine learning. However, training them can present challenges. One notable challenge is the vanishing and exploding gradient problem during backpropagation. The choice of activation functions can significantly influence these issues.\n","\n","## Activation Functions\n","\n","Activation functions introduce non-linearity into the network, enabling it to learn from the error and make adjustments, which is essential for learning complex patterns.\n","\n","### 1. Sigmoid\n"," $$f(x) = \\frac{1}{1 + e^{-x}} $$\n","- **Range**: [0, 1]\n","- **Challenge**: Can lead to vanishing gradients in deep networks due to its saturation for large positive or negative inputs.\n","- **Gradient**: \n"," $\\frac{\\partial f(x)}{\\partial x} = f(x)(1 - f(x)) $\n","\n","### 2. ReLU (Rectified Linear Unit)\n","$$ f(x) = \\max(0, x) $$\n","- **Range**: $[0, \\infty)$\n","- **Benefit**: Mitigates the vanishing gradient problem.\n","- **Challenge**: Can lead to exploding gradients if not carefully initialized or regularized.\n","\n","### 3. Leaky ReLU\n","$$f(x) = \n","\\begin{cases} \n","x & \\text{if } x > 0 \\\\\n","\\alpha x & \\text{otherwise}\n","\\end{cases} \n","$$\n","- **Range**: $(- \\infty, \\infty)$\n","- **Benefit**: Addresses the \"dying ReLU\" problem where neurons never activate.\n","- **Parameter**: $\\alpha$ is a small positive constant (e.g., $0.01$).\n","\n","### 4. Tanh\n","$$ f(x) = \\frac{e^x - e^{-x}}{e^x + e^{-x}} $$\n","- **Range**: $[-1, 1]$\n","- **Note**: It's a scaled and shifted version of the sigmoid function. In fact, it scales the sigmoid's output by 2 and shifts it down by 1.\n","\n","### 5. Arctangent\n","$$ f(x) = \\arctan(x) $$\n","- **Range**: $[- \\frac{\\pi}{2}, \\frac{\\pi}{2}]$\n","\n","## Gradient Dynamics\n","\n","- **Vanishing Gradient**: Occurs when gradients approach zero, stalling the training process. Often observed with activation functions that squash their inputs into a small range, especially when inputs are large.\n","\n","- **Exploding Gradient**: Occurs when gradients grow too large, causing divergent behavior. This can be due to deep networks, large parameter values, or particular activation functions.\n","\n","## Mitigating Strategies\n","\n","1. **Proper Initialization**: Initialize weights in a manner that neither amplifies nor diminishes gradients during backpropagation.\n","2. **Shortcuts/Residual connections**: Used in architectures like ResNets, these allow gradients to skip layers and mitigate the vanishing gradient issue.\n","3. **Gradient Clipping**: A technique to prevent gradients from exceeding a threshold and thus prevent the exploding gradient problem.\n","\n","## Diagnostic Code\n","\n","The code provided is a tool designed to help visualize the behavior of different activation functions in deep neural networks. By examining activations, weights, and gradients, we can gain insight into and potentially address the challenges associated with them."]},{"cell_type":"code","execution_count":1,"id":"e62febcb","metadata":{"execution":{"iopub.execute_input":"2023-07-27T13:36:27.70327Z","iopub.status.busy":"2023-07-27T13:36:27.702716Z","iopub.status.idle":"2023-07-27T13:36:49.998539Z","shell.execute_reply":"2023-07-27T13:36:49.996867Z"},"jupyter":{"source_hidden":true},"papermill":{"duration":22.305582,"end_time":"2023-07-27T13:36:50.001817","exception":false,"start_time":"2023-07-27T13:36:27.696235","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n","  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n","caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n","  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n","/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n","caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n","  warnings.warn(f\"file system plugins are not loaded: {e}\")\n"]},{"name":"stdout","output_type":"stream","text":["\n","[LEAKY_RELU] Extracting neuron activations (outputs) for input data [[ 1. ]\n"," [ 0.5]\n"," [-1. ]\n"," [-0.5]]. This helps us understand how data flows through the network.\n","\n","[LEAKY_RELU] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[LEAKY_RELU] Computing gradients to understand how much the error will change if the weights are adjusted. This is crucial for learning.\n","\n","[LEAKY_RELU] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[LEAKY_RELU] Computing gradients to understand how much the error will change if the weights are adjusted. This is crucial for learning.\n","\n","[LEAKY_RELU] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[LEAKY_RELU] Checking for potential gradient issues like vanishing or exploding gradients which can hinder learning.\n","[Warning] leaky_relu has potential vanishing gradient issues at layers: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 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size 2000x1500 with 3 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n","[SIGMOID] Extracting neuron activations (outputs) for input data [[ 1. ]\n"," [ 0.5]\n"," [-1. ]\n"," [-0.5]]. This helps us understand how data flows through the network.\n","\n","[RELU] Extracting neuron activations (outputs) for input data [[ 1. ]\n"," [ 0.5]\n"," [-1. ]\n"," [-0.5]]. This helps us understand how data flows through the network.\n","\n","[TANH] Extracting neuron activations (outputs) for input data [[ 1. ]\n"," [ 0.5]\n"," [-1. ]\n"," [-0.5]]. This helps us understand how data flows through the network.\n","\n","[SIGMOID] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[RELU] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[TANH] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[SIGMOID] Computing gradients to understand how much the error will change if the weights are adjusted. This is crucial for learning.\n","\n","[SIGMOID] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[RELU] Computing gradients to understand how much the error will change if the weights are adjusted. This is crucial for learning.\n","\n","[RELU] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[TANH] Computing gradients to understand how much the error will change if the weights are adjusted. This is crucial for learning.\n","\n","[TANH] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[SIGMOID] Computing gradients to understand how much the error will change if the weights are adjusted. This is crucial for learning.\n","\n","[SIGMOID] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[SIGMOID] Checking for potential gradient issues like vanishing or exploding gradients which can hinder learning.\n","[Warning] sigmoid has potential vanishing gradient issues at layers: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n","\n","[RELU] Computing gradients to understand how much the error will change if the weights are adjusted. This is crucial for learning.\n","\n","[RELU] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[RELU] Checking for potential gradient issues like vanishing or exploding gradients which can hinder learning.\n","[Warning] relu has potential vanishing gradient issues at layers: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n","\n","[TANH] Computing gradients to understand how much the error will change if the weights are adjusted. This is crucial for learning.\n","\n","[TANH] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\n","\n","[TANH] Checking for potential gradient issues like vanishing or exploding gradients which can hinder learning.\n","[Warning] tanh has potential vanishing gradient issues at layers: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 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size 2000x1500 with 3 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import os\n","import warnings\n","import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.layers import Dense, LeakyReLU\n","\n","# Suppress TensorFlow logs and Python warnings for clearer output\n","os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n","warnings.filterwarnings(\"ignore\")\n","\n","def create_network(activation, layers, units, output_units):\n","    \"\"\" Create a neural network model based on the given activation function.\"\"\"\n","    model = tf.keras.Sequential()\n","    \n","    for _ in range(layers):\n","        if activation == 'leaky_relu':\n","            model.add(Dense(units))\n","            model.add(LeakyReLU(alpha=0.01))\n","        else:\n","            model.add(Dense(units, activation=activation))\n","            \n","    model.add(Dense(output_units))\n","    model.build(input_shape=(None, 1))\n","    return model\n","\n","def get_activations_for_model(model, data, activation_type):\n","    \"\"\"Extract activations for the given data.\"\"\"\n","    print(f\"\\n[{activation_type.upper()}] Extracting neuron activations (outputs) for input data {data.numpy()}. This helps us understand how data flows through the network.\")\n","    return [tf.keras.Model(inputs=model.input, outputs=layer.output).predict(data, verbose=0) for layer in model.layers]\n","\n","def get_weights_for_model(model, activation_type):\n","    \"\"\"Extract weights from the model.\"\"\"\n","    print(f\"\\n[{activation_type.upper()}] Retrieving the learned weights from the model. These weights represent the strength of connections between neurons.\")\n","    return [layer.get_weights()[0] for layer in model.layers if layer.get_weights()]\n","\n","def compute_gradients(model, data, activation_type):\n","    \"\"\"Compute gradients for the given data.\"\"\"\n","    print(f\"\\n[{activation_type.upper()}] Computing gradients to understand how much the error will change if the weights are adjusted. This is crucial for learning.\")\n","    with tf.GradientTape(persistent=True) as tape:\n","        tape.watch(data)\n","        predictions = model(data)\n","    \n","    gradient_input = tape.gradient(predictions, data).numpy()\n","    weights_tf = [tf.convert_to_tensor(weight) for weight in get_weights_for_model(model, activation_type)]\n","    gradient_weights = [tape.gradient(predictions, weight).numpy() if tape.gradient(predictions, weight) else np.zeros_like(weight.numpy()) for weight in weights_tf]\n","    del tape\n","    return gradient_input, gradient_weights\n","\n","def identify_gradient_issues(gradient_weights, activation_type):\n","    \"\"\"Identify potential gradient issues.\"\"\"\n","    print(f\"\\n[{activation_type.upper()}] Checking for potential gradient issues like vanishing or exploding gradients which can hinder learning.\")\n","    gradient_issues = {\"vanishing\": [], \"exploding\": []}\n","    for idx, gradient in enumerate(gradient_weights):\n","        if np.linalg.norm(gradient) < 1e-5:\n","            gradient_issues[\"vanishing\"].append(idx)\n","        elif np.linalg.norm(gradient) > 1e3:\n","            gradient_issues[\"exploding\"].append(idx)\n","    return gradient_issues\n","\n","def plot_function(data, label, title, linestyle='-'):\n","    \"\"\"Plot the function based on given data.\"\"\"\n","    avg_magnitudes = [np.mean(np.abs(d)) for d in data]\n","    plt.plot(avg_magnitudes, label=label, linestyle=linestyle)\n","\n","def plot_diagnostics(models, data):\n","    \"\"\"Visualize the behavior of different activation functions.\"\"\"\n","    plt.figure(figsize=(20, 15))\n","    diagnostics = [(\"Activations\", '-'), (\"Weights\", '--'), (\"Gradients\", ':')]\n","    \n","    for idx, (title, linestyle) in enumerate(diagnostics, 1):\n","        plt.subplot(3, 1, idx)\n","        for activation_type, model in models.items():\n","            if title == \"Activations\":\n","                data_to_plot = get_activations_for_model(model, data, activation_type)  # pass the activation_type here\n","            elif title == \"Weights\":\n","                data_to_plot = get_weights_for_model(model, activation_type)  # and here\n","            else:\n","                _, gradient_weights = compute_gradients(model, data, activation_type)  # and here\n","                data_to_plot = gradient_weights\n","            \n","            plot_function(data_to_plot, activation_type, title, linestyle)\n","        \n","        plt.xlabel('Layer')\n","        plt.ylabel('Magnitude')\n","        plt.title(title)\n","        plt.legend()\n","        with warnings.catch_warnings():\n","            warnings.simplefilter(\"ignore\")\n","            plt.tight_layout()\n","    \n","    for activation_type, model in models.items():\n","        _, gradient_weights = compute_gradients(model, data, activation_type)  # and here\n","        gradient_issues = identify_gradient_issues(gradient_weights, activation_type)  # and here\n","        if gradient_issues[\"vanishing\"]:\n","            print(f\"[Warning] {activation_type} has potential vanishing gradient issues at layers: {gradient_issues['vanishing']}\")\n","        if gradient_issues[\"exploding\"]:\n","            print(f\"[Warning] {activation_type} has potential exploding gradient issues at layers: {gradient_issues['exploding']}\")\n","    \n","    plt.show()\n","\n","\n","# Dedicated analysis for Leaky ReLU\n","leaky_relu_model = create_network('leaky_relu', 10, 10, 1)\n","data_batch = tf.constant([[1.0], [0.5], [-1.0], [-0.5]])\n","plot_diagnostics({'leaky_relu': leaky_relu_model}, data_batch)\n","\n","# Comparison with other activation functions\n","ACTIVATIONS = ['sigmoid', 'relu', 'tanh']\n","models_dict = {activation: create_network(activation, 10, 10, 1) for activation in ACTIVATIONS}\n","plot_diagnostics(models_dict, data_batch)"]},{"cell_type":"code","execution_count":2,"id":"7d7415ef","metadata":{"execution":{"iopub.execute_input":"2023-07-27T13:36:50.025082Z","iopub.status.busy":"2023-07-27T13:36:50.024643Z","iopub.status.idle":"2023-07-27T13:37:16.617539Z","shell.execute_reply":"2023-07-27T13:37:16.616125Z"},"jupyter":{"source_hidden":true},"papermill":{"duration":26.608257,"end_time":"2023-07-27T13:37:16.620422","exception":false,"start_time":"2023-07-27T13:36:50.012165","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","Analysis for SIGMOID Activation\n","----------------------------------------\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Activation: 0.5000, Standard Deviation: 0.0725\n","Layer 2 - Mean Activation: 0.5566, Standard Deviation: 0.1253\n","Layer 3 - Mean Activation: 0.5169, Standard Deviation: 0.1044\n","Layer 4 - Mean Activation: 0.4949, Standard Deviation: 0.1068\n","Layer 5 - Mean Activation: 0.4925, Standard Deviation: 0.1212\n","Layer 6 - Mean Activation: 0.4232, Standard Deviation: 0.0991\n","Layer 7 - Mean Activation: 0.5565, Standard Deviation: 0.1302\n","Layer 8 - Mean Activation: 0.4332, Standard Deviation: 0.1227\n","Layer 9 - Mean Activation: 0.4744, Standard Deviation: 0.0925\n","Layer 10 - Mean Activation: 0.5365, Standard Deviation: 0.1233\n","Layer 11 - Mean Activation: -0.2810, Standard Deviation: 0.0000\n"]},{"data":{"image/png":"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size 1000x600 with 2 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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Weight: 0.1176, Standard Deviation: 0.3576\n","Layer 2 - Mean Weight: 0.0492, Standard Deviation: 0.3069\n","Layer 3 - Mean Weight: 0.0261, Standard Deviation: 0.3405\n","Layer 4 - Mean Weight: -0.0068, Standard Deviation: 0.2987\n","Layer 5 - Mean Weight: 0.0061, Standard Deviation: 0.3085\n","Layer 6 - Mean Weight: -0.0622, Standard Deviation: 0.3042\n","Layer 7 - Mean Weight: 0.0500, Standard Deviation: 0.3245\n","Layer 8 - Mean Weight: -0.0666, Standard Deviation: 0.3127\n","Layer 9 - Mean Weight: -0.0312, Standard Deviation: 0.3081\n","Layer 10 - Mean Weight: 0.0362, Standard Deviation: 0.3165\n","Layer 11 - Mean Weight: -0.0648, Standard Deviation: 0.4390\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Gradient: -0.0000, Standard Deviation: 0.0000\n","Layer 2 - Mean Gradient: -0.0000, Standard Deviation: 0.0000\n","Layer 3 - Mean Gradient: -0.0000, Standard Deviation: 0.0000\n","Layer 4 - Mean Gradient: -0.0000, Standard Deviation: 0.0000\n","Layer 5 - Mean Gradient: 0.0000, Standard Deviation: 0.0000\n","Layer 6 - Mean Gradient: 0.0000, Standard Deviation: 0.0000\n","Layer 7 - Mean Gradient: 0.0000, Standard Deviation: 0.0000\n","Layer 8 - Mean Gradient: 0.0000, Standard Deviation: 0.0001\n","Layer 9 - Mean Gradient: -0.0001, Standard Deviation: 0.0001\n","Layer 10 - Mean Gradient: -0.0001, Standard Deviation: 0.0003\n","Layer 11 - Mean Gradient: 0.0000, Standard Deviation: 0.0006\n","Layer 12 - Mean Gradient: 0.0000, Standard Deviation: 0.0012\n","Layer 13 - Mean Gradient: -0.0002, Standard Deviation: 0.0021\n","Layer 14 - Mean Gradient: -0.0005, Standard Deviation: 0.0049\n","Layer 15 - Mean Gradient: -0.0008, Standard Deviation: 0.0120\n","Layer 16 - Mean Gradient: -0.0014, Standard Deviation: 0.0210\n","Layer 17 - Mean Gradient: 0.0033, Standard Deviation: 0.0510\n","Layer 18 - Mean Gradient: 0.0076, Standard Deviation: 0.1134\n","Layer 19 - Mean Gradient: -0.0341, Standard Deviation: 0.1958\n","Layer 20 - Mean Gradient: -0.0719, Standard Deviation: 0.4048\n","Layer 21 - Mean Gradient: 2.1459, Standard Deviation: 0.4933\n","Layer 22 - Mean Gradient: 4.0000, Standard Deviation: 0.0000\n","\n","\n","\n","\n","Analysis for RELU Activation\n","----------------------------------------\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Activation: 0.1225, Standard Deviation: 0.1688\n","Layer 2 - Mean Activation: 0.0842, Standard Deviation: 0.1224\n","Layer 3 - Mean Activation: 0.0994, Standard Deviation: 0.1033\n","Layer 4 - Mean Activation: 0.0453, Standard Deviation: 0.0765\n","Layer 5 - Mean Activation: 0.0204, Standard Deviation: 0.0376\n","Layer 6 - Mean Activation: 0.0190, Standard Deviation: 0.0321\n","Layer 7 - Mean Activation: 0.0076, Standard Deviation: 0.0154\n","Layer 8 - Mean Activation: 0.0065, Standard Deviation: 0.0125\n","Layer 9 - Mean Activation: 0.0030, Standard Deviation: 0.0058\n","Layer 10 - Mean Activation: 0.0031, Standard Deviation: 0.0047\n","Layer 11 - Mean Activation: 0.0062, Standard Deviation: 0.0070\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 2 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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Weight: -0.0975, Standard Deviation: 0.3601\n","Layer 2 - Mean Weight: -0.0254, Standard Deviation: 0.3109\n","Layer 3 - Mean Weight: 0.0543, Standard Deviation: 0.3084\n","Layer 4 - Mean Weight: -0.0202, Standard Deviation: 0.3215\n","Layer 5 - Mean Weight: -0.0261, Standard Deviation: 0.3074\n","Layer 6 - Mean Weight: 0.0320, Standard Deviation: 0.3214\n","Layer 7 - Mean Weight: -0.0593, Standard Deviation: 0.2885\n","Layer 8 - Mean Weight: -0.0175, Standard Deviation: 0.3125\n","Layer 9 - Mean Weight: -0.0010, Standard Deviation: 0.3154\n","Layer 10 - Mean Weight: 0.0429, Standard Deviation: 0.3039\n","Layer 11 - Mean Weight: 0.1757, Standard Deviation: 0.3981\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Gradient: -0.0085, Standard Deviation: 0.0486\n","Layer 2 - Mean Gradient: 0.0088, Standard Deviation: 0.0652\n","Layer 3 - Mean Gradient: 0.0003, Standard Deviation: 0.0152\n","Layer 4 - Mean Gradient: 0.0025, Standard Deviation: 0.0685\n","Layer 5 - Mean Gradient: 0.0005, Standard Deviation: 0.0135\n","Layer 6 - Mean Gradient: 0.0049, Standard Deviation: 0.0856\n","Layer 7 - Mean Gradient: -0.0003, Standard Deviation: 0.0147\n","Layer 8 - Mean Gradient: -0.0092, Standard Deviation: 0.1014\n","Layer 9 - Mean Gradient: 0.0028, Standard Deviation: 0.0150\n","Layer 10 - Mean Gradient: 0.0517, Standard Deviation: 0.1453\n","Layer 11 - Mean Gradient: -0.0010, Standard Deviation: 0.0097\n","Layer 12 - Mean Gradient: -0.0225, Standard Deviation: 0.1815\n","Layer 13 - Mean Gradient: 0.0031, Standard Deviation: 0.0075\n","Layer 14 - Mean Gradient: 0.0774, Standard Deviation: 0.2670\n","Layer 15 - Mean Gradient: 0.0011, Standard Deviation: 0.0057\n","Layer 16 - Mean Gradient: 0.2205, Standard Deviation: 0.6635\n","Layer 17 - Mean Gradient: -0.0006, Standard Deviation: 0.0135\n","Layer 18 - Mean Gradient: -0.1776, Standard Deviation: 1.3910\n","Layer 19 - Mean Gradient: 0.0012, Standard Deviation: 0.0070\n","Layer 20 - Mean Gradient: 0.4644, Standard Deviation: 1.4704\n","Layer 21 - Mean Gradient: 0.0123, Standard Deviation: 0.0112\n","Layer 22 - Mean Gradient: 4.0000, Standard Deviation: 0.0000\n","\n","\n","\n","\n","Analysis for TANH Activation\n","----------------------------------------\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Activation: 0.0000, Standard Deviation: 0.3027\n","Layer 2 - Mean Activation: 0.0000, Standard Deviation: 0.2787\n","Layer 3 - Mean Activation: 0.0000, Standard Deviation: 0.3277\n","Layer 4 - Mean Activation: 0.0000, Standard Deviation: 0.1693\n","Layer 5 - Mean Activation: 0.0000, Standard Deviation: 0.2395\n","Layer 6 - Mean Activation: 0.0000, Standard Deviation: 0.2056\n","Layer 7 - Mean Activation: 0.0000, Standard Deviation: 0.1739\n","Layer 8 - Mean Activation: 0.0000, Standard Deviation: 0.1747\n","Layer 9 - Mean Activation: 0.0000, Standard Deviation: 0.1968\n","Layer 10 - Mean Activation: 0.0000, Standard Deviation: 0.1691\n","Layer 11 - Mean Activation: 0.0000, Standard Deviation: 0.0999\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 2 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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Weight: -0.0187, Standard Deviation: 0.4154\n","Layer 2 - Mean Weight: -0.0508, Standard Deviation: 0.3272\n","Layer 3 - Mean Weight: 0.0235, Standard Deviation: 0.3414\n","Layer 4 - Mean Weight: 0.0175, Standard Deviation: 0.3219\n","Layer 5 - Mean Weight: 0.0121, Standard Deviation: 0.3326\n","Layer 6 - Mean Weight: 0.0248, Standard Deviation: 0.3303\n","Layer 7 - Mean Weight: -0.0363, Standard Deviation: 0.3253\n","Layer 8 - Mean Weight: 0.0087, Standard Deviation: 0.3063\n","Layer 9 - Mean Weight: -0.0341, Standard Deviation: 0.3115\n","Layer 10 - Mean Weight: -0.0160, Standard Deviation: 0.3153\n","Layer 11 - Mean Weight: -0.1405, Standard Deviation: 0.3495\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Gradient: 0.0000, Standard Deviation: 0.0000\n","Layer 2 - Mean Gradient: -0.0549, Standard Deviation: 0.2619\n","Layer 3 - Mean Gradient: -0.0000, Standard Deviation: 0.0000\n","Layer 4 - Mean Gradient: 0.0144, Standard Deviation: 0.2913\n","Layer 5 - Mean Gradient: -0.0000, Standard Deviation: 0.0000\n","Layer 6 - Mean Gradient: 0.1828, Standard Deviation: 0.3485\n","Layer 7 - Mean Gradient: 0.0000, Standard Deviation: 0.0000\n","Layer 8 - Mean Gradient: 0.2514, Standard Deviation: 0.3983\n","Layer 9 - Mean Gradient: 0.0000, Standard Deviation: 0.0000\n","Layer 10 - Mean Gradient: 0.1509, Standard Deviation: 0.5635\n","Layer 11 - Mean Gradient: 0.0000, Standard Deviation: 0.0000\n","Layer 12 - Mean Gradient: 0.0530, Standard Deviation: 0.7302\n","Layer 13 - Mean Gradient: -0.0000, Standard Deviation: 0.0000\n","Layer 14 - Mean Gradient: 0.2709, Standard Deviation: 0.8618\n","Layer 15 - Mean Gradient: -0.0000, Standard Deviation: 0.0000\n","Layer 16 - Mean Gradient: 0.1960, Standard Deviation: 1.2923\n","Layer 17 - Mean Gradient: -0.0000, Standard Deviation: 0.0000\n","Layer 18 - Mean Gradient: -0.3360, Standard Deviation: 1.1485\n","Layer 19 - Mean Gradient: 0.0000, Standard Deviation: 0.0000\n","Layer 20 - Mean Gradient: -0.5444, Standard Deviation: 1.3679\n","Layer 21 - Mean Gradient: -0.0000, Standard Deviation: 0.0000\n","Layer 22 - Mean Gradient: 4.0000, Standard Deviation: 0.0000\n","\n","\n","\n","\n","Analysis for LEAKY_RELU Activation\n","----------------------------------------\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Activation: -0.0000, Standard Deviation: 0.3718\n","Layer 2 - Mean Activation: 0.1540, Standard Deviation: 0.2131\n","Layer 3 - Mean Activation: -0.0874, Standard Deviation: 0.2933\n","Layer 4 - Mean Activation: 0.0818, Standard Deviation: 0.1579\n","Layer 5 - Mean Activation: 0.0008, Standard Deviation: 0.2019\n","Layer 6 - Mean Activation: 0.0778, Standard Deviation: 0.1223\n","Layer 7 - Mean Activation: -0.0468, Standard Deviation: 0.1192\n","Layer 8 - Mean Activation: 0.0244, Standard Deviation: 0.0659\n","Layer 9 - Mean Activation: -0.0032, Standard Deviation: 0.0734\n","Layer 10 - Mean Activation: 0.0262, Standard Deviation: 0.0467\n","Layer 11 - Mean Activation: -0.0343, Standard Deviation: 0.0588\n","Layer 12 - Mean Activation: 0.0090, Standard Deviation: 0.0208\n","Layer 13 - Mean Activation: -0.0016, Standard Deviation: 0.0142\n","Layer 14 - Mean Activation: 0.0032, Standard Deviation: 0.0056\n","Layer 15 - Mean Activation: 0.0051, Standard Deviation: 0.0052\n","Layer 16 - Mean Activation: 0.0056, Standard Deviation: 0.0044\n","Layer 17 - Mean Activation: 0.0009, Standard Deviation: 0.0069\n","Layer 18 - Mean Activation: 0.0032, Standard Deviation: 0.0047\n","Layer 19 - Mean Activation: 0.0037, Standard Deviation: 0.0064\n","Layer 20 - Mean Activation: 0.0048, Standard Deviation: 0.0050\n","Layer 21 - Mean Activation: 0.0133, Standard Deviation: 0.0045\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 2 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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Weight: -0.1183, Standard Deviation: 0.4552\n","Layer 2 - Mean Weight: -0.0331, Standard Deviation: 0.3074\n","Layer 3 - Mean Weight: -0.0589, Standard Deviation: 0.3314\n","Layer 4 - Mean Weight: 0.0073, Standard Deviation: 0.2916\n","Layer 5 - Mean Weight: -0.0209, Standard Deviation: 0.3104\n","Layer 6 - Mean Weight: -0.0508, Standard Deviation: 0.3059\n","Layer 7 - Mean Weight: 0.0169, Standard Deviation: 0.3282\n","Layer 8 - Mean Weight: -0.0007, Standard Deviation: 0.3055\n","Layer 9 - Mean Weight: 0.0031, Standard Deviation: 0.3373\n","Layer 10 - Mean Weight: 0.0250, Standard Deviation: 0.2969\n","Layer 11 - Mean Weight: 0.1371, Standard Deviation: 0.4251\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Layer 1 - Mean Gradient: 0.0066, Standard Deviation: 0.0401\n","Layer 2 - Mean Gradient: 0.0153, Standard Deviation: 0.0516\n","Layer 3 - Mean Gradient: 0.0059, Standard Deviation: 0.0252\n","Layer 4 - Mean Gradient: 0.0349, Standard Deviation: 0.0819\n","Layer 5 - Mean Gradient: 0.0041, Standard Deviation: 0.0270\n","Layer 6 - Mean Gradient: 0.0434, Standard Deviation: 0.1374\n","Layer 7 - Mean Gradient: 0.0041, Standard Deviation: 0.0351\n","Layer 8 - Mean Gradient: 0.0598, Standard Deviation: 0.2522\n","Layer 9 - Mean Gradient: 0.0004, Standard Deviation: 0.0221\n","Layer 10 - Mean Gradient: 0.0228, Standard Deviation: 0.3509\n","Layer 11 - Mean Gradient: 0.0033, Standard Deviation: 0.0382\n","Layer 12 - Mean Gradient: 0.1271, Standard Deviation: 0.7679\n","Layer 13 - Mean Gradient: 0.0027, Standard Deviation: 0.0218\n","Layer 14 - Mean Gradient: 0.2882, Standard Deviation: 1.0016\n","Layer 15 - Mean Gradient: 0.0018, Standard Deviation: 0.0081\n","Layer 16 - Mean Gradient: 0.5457, Standard Deviation: 1.2548\n","Layer 17 - Mean Gradient: 0.0035, Standard Deviation: 0.0059\n","Layer 18 - Mean Gradient: 0.6345, Standard Deviation: 0.8189\n","Layer 19 - Mean Gradient: 0.0011, Standard Deviation: 0.0081\n","Layer 20 - Mean Gradient: 0.3217, Standard Deviation: 1.4932\n","Layer 21 - Mean Gradient: 0.0192, Standard Deviation: 0.0178\n","Layer 22 - Mean Gradient: 4.0000, Standard Deviation: 0.0000\n","\n","\n","\n"]}],"source":["import os\n","import warnings\n","import tensorflow as tf\n","import numpy as np\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.layers import Dense, LeakyReLU\n","\n","# Suppress TensorFlow logs and Python warnings for clearer output\n","os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n","warnings.filterwarnings(\"ignore\")\n","\n","def create_network(activation, layers, units, output_units):\n","    \"\"\" \n","    Create a neural network model based on the given activation function.\n","    \"\"\"\n","    model = tf.keras.Sequential()\n","    \n","    for _ in range(layers):\n","        if activation == 'leaky_relu':\n","            model.add(Dense(units))\n","            model.add(LeakyReLU(alpha=0.01))\n","        else:\n","            model.add(Dense(units, activation=activation))\n","            \n","    model.add(Dense(output_units))\n","    model.build(input_shape=(None, 1))\n","    return model\n","\n","def get_activations_for_model(model, data):\n","    \"\"\"\n","    Extract activations for the given data.\n","    \"\"\"\n","    return [tf.keras.Model(inputs=model.input, outputs=layer.output).predict(data, verbose=0) for layer in model.layers]\n","\n","def get_weights_for_model(model):\n","    \"\"\"\n","    Extract weights from the model.\n","    \"\"\"\n","    return [layer.get_weights()[0] for layer in model.layers if layer.get_weights()]\n","\n","def compute_gradients(model, data):\n","    \"\"\"\n","    Compute gradients for the given data.\n","    \"\"\"\n","    with tf.GradientTape(persistent=True) as tape:\n","        tape.watch(data)\n","        predictions = model(data)\n","    \n","    gradient_input = tape.gradient(predictions, data)\n","    gradient_weights = []\n","    \n","    for layer in model.layers:\n","        weights = layer.trainable_weights  # Ensure we only consider trainable weights\n","        if weights:\n","            gradients = tape.gradient(predictions, weights)\n","            gradient_weights.extend(gradients)\n","    \n","    return gradient_input, gradient_weights\n","\n","\n","def plot_activations(activation_type, model, data):\n","    \"\"\"\n","    Plot activations for the given model and data.\n","    \"\"\"\n","    activations = get_activations_for_model(model, data)\n","    for idx, activation in enumerate(activations):\n","        plt.figure(figsize=(6, 4))\n","        plt.hist(activation, bins=50)\n","        plt.title(f\"{activation_type} Activations in Layer {idx+1}\")\n","        plt.xlabel(\"Activation Value\")\n","        plt.ylabel(\"Frequency\")\n","        plt.grid(True)\n","        plt.tight_layout()\n","        plt.show()\n","\n","def plot_weights(activation_type, model):\n","    \"\"\"\n","    Plot weight distributions for the given model.\n","    \"\"\"\n","    weights = get_weights_for_model(model)\n","    for idx, weight in enumerate(weights):\n","        plt.figure(figsize=(6, 4))\n","        plt.hist(weight.flatten(), bins=50)\n","        plt.title(f\"{activation_type} Weights in Layer {idx+1}\")\n","        plt.xlabel(\"Weight Value\")\n","        plt.ylabel(\"Frequency\")\n","        plt.grid(True)\n","        plt.tight_layout()\n","        plt.show()\n","\n","def plot_gradients(activation_type, model, data):\n","    \"\"\"\n","    Plot gradients for the given model and data.\n","    \"\"\"\n","    _, gradient_weights = compute_gradients(model, data)\n","    fig, axes = plt.subplots(1, len(gradient_weights), figsize=(6 * len(gradient_weights), 4))\n","    for idx, gradient in enumerate(gradient_weights):\n","        if len(gradient_weights) > 1:\n","            ax = axes[idx]\n","        else:\n","            ax = axes\n","        ax.hist(gradient.numpy().flatten(), bins=50)\n","        ax.set_title(f\"{activation_type} Gradients in Layer {idx+1}\")\n","        ax.set_xlabel(\"Gradient Value\")\n","        ax.set_ylabel(\"Frequency\")\n","        ax.grid(True)\n","    plt.tight_layout()\n","    plt.show()\n","\n","def plot_summary(data_list, title, data_type):\n","    \"\"\"\n","    Plot boxplots and summary statistics for activations, weights, or gradients.\n","    \"\"\"\n","    fig, ax = plt.subplots(figsize=(10, 6))\n","    if isinstance(data_list[0], tf.Tensor):  # Check if the data is still in tensor format\n","        sns.boxplot(data=[d.numpy().flatten() for d in data_list], ax=ax)\n","    else:\n","        sns.boxplot(data=[d.flatten() for d in data_list], ax=ax)  # For numpy arrays\n","    ax.set_title(f\"{title} {data_type} Distribution across Layers\")\n","    ax.set_xlabel(\"Layer\")\n","    ax.set_ylabel(f\"{data_type} Value\")\n","    plt.show()\n","\n","    # Display summary statistics\n","    for idx, data in enumerate(data_list):\n","        if isinstance(data, tf.Tensor):  # Check if the data is still in tensor format\n","            mean_val = np.mean(data.numpy())\n","            std_val = np.std(data.numpy())\n","        else:\n","            mean_val = np.mean(data)\n","            std_val = np.std(data)\n","        print(f\"Layer {idx+1} - Mean {data_type}: {mean_val:.4f}, Standard Deviation: {std_val:.4f}\")\n","\n","def plot_activations_heatmap(activation_type, model, data):\n","    \"\"\"\n","    Plot heatmap for activations across layers for multiple inputs.\n","    \"\"\"\n","    activations = get_activations_for_model(model, data)\n","    \n","    # Check if activations are in tensor format or numpy arrays\n","    if isinstance(activations[0], tf.Tensor):\n","        activations_matrix = np.hstack([act.numpy() for act in activations])\n","    else:\n","        activations_matrix = np.hstack(activations)  # Directly stack if already numpy arrays\n","    \n","    plt.figure(figsize=(10, 6))\n","    sns.heatmap(activations_matrix, cmap='viridis', cbar=True)\n","    plt.title(f\"{activation_type} Activations across Layers\")\n","    plt.xlabel(\"Layer\")\n","    plt.ylabel(\"Input Sample\")\n","    plt.show()\n","\n","def analyze_networks(models, data):\n","    \"\"\"\n","    Analyze activations, weights, and gradients for all models.\n","    \"\"\"\n","    for activation_type, model in models.items():\n","        print(f\"\\nAnalysis for {activation_type.upper()} Activation\\n{'-'*40}\")\n","        \n","        activations = get_activations_for_model(model, data)\n","        plot_summary(activations, activation_type, \"Activation\")\n","        plot_activations_heatmap(activation_type, model, data)\n","\n","        weights = get_weights_for_model(model)\n","        plot_summary(weights, activation_type, \"Weight\")\n","\n","        _, gradient_weights = compute_gradients(model, data)\n","        plot_summary(gradient_weights, activation_type, \"Gradient\")\n","\n","        print(\"\\n\\n\")\n","\n","# Test the functions\n","ACTIVATIONS = ['sigmoid', 'relu', 'tanh', 'leaky_relu']\n","models_dict = {activation: create_network(activation, 10, 10, 1) for activation in ACTIVATIONS}\n","data_batch = tf.constant([[1.0], [0.5], [-1.0], [-0.5]])\n","analyze_networks(models_dict, data_batch)\n"]},{"cell_type":"markdown","id":"6c2a7181","metadata":{"papermill":{"duration":0.025537,"end_time":"2023-07-27T13:37:16.673286","exception":false,"start_time":"2023-07-27T13:37:16.647749","status":"completed"},"tags":[]},"source":["# Why Leaky ReLU exhibits a distinct behavior?\n","\n","- **No Zero Slope**: Unlike standard ReLU, Leaky ReLU retains a small gradient for negative inputs. This prevents 'dead neurons' – neurons that never activate.\n","  \n","- **Deep Networks**: Activation functions' effects intensify in deep networks. For sigmoid, gradients can vanish in deep layers due to minuscule gradient values. Standard ReLU's gradients can vanish for negative inputs or stay consistent for positive ones. However, Leaky ReLU's gradient remains slightly positive for negative inputs. __In deeper layers, these minute gradients can compound, resulting in significant activations__.\n","\n","- **Initialization**: The manner in which weights and biases are initialized can sway activation behaviors. If initial conditions push Leaky ReLU's activations mostly towards negative values, growing network depth might amplify these activations, especially if negative values consistently pass through Leaky ReLU.\n","\n","- **Network Design**: The overall architecture, encompassing neuron count, layer structure, and inter-neuronal connections, can modulate activation behaviors. Certain designs might amplify or suppress activations."]},{"cell_type":"code","execution_count":3,"id":"3f4bf0af","metadata":{"execution":{"iopub.execute_input":"2023-07-27T13:37:16.72648Z","iopub.status.busy":"2023-07-27T13:37:16.726044Z","iopub.status.idle":"2023-07-27T13:37:17.235286Z","shell.execute_reply":"2023-07-27T13:37:17.234162Z"},"jupyter":{"source_hidden":true},"papermill":{"duration":0.539152,"end_time":"2023-07-27T13:37:17.238057","exception":false,"start_time":"2023-07-27T13:37:16.698905","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","\n","def leaky_relu(x, alpha=0.01):\n","    return np.where(x > 0, x, alpha * x)\n","\n","def updated_leaky_relu_plot(alpha=0.2):\n","    # Define x range\n","    x = np.linspace(-10, 5, 600)\n","\n","    # Compute Leaky ReLU values\n","    y = leaky_relu(x, alpha=alpha)\n","\n","    # Plotting\n","    plt.figure(figsize=(10, 8))\n","    plt.plot(x, y, 'b--', linewidth=2)  # Dashed line style\n","    plt.title('Leaky ReLU Activation Function', fontsize=15)\n","    plt.xlabel('x', fontsize=14)\n","    plt.ylabel('Leaky ReLU(x)', fontsize=14)  # Updated y-label\n","    plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)  # Updated grid style\n","    plt.axhline(0, color='black', linewidth=0.5)\n","    plt.axvline(0, color='black', linewidth=0.5)\n","\n","    # Annotate the value of alpha\n","    plt.annotate(f'α = {alpha}', xy=(-9.5, -0.5), color='red', fontsize=12)\n","\n","    plt.tight_layout()\n","    plt.show()\n","\n","# Generate the updated plot\n","updated_leaky_relu_plot()"]},{"cell_type":"markdown","id":"e4427c5e","metadata":{"papermill":{"duration":0.025419,"end_time":"2023-07-27T13:37:17.289998","exception":false,"start_time":"2023-07-27T13:37:17.264579","status":"completed"},"tags":[]},"source":["### Leaky ReLU introduces a slight slope for negative inputs, preventing neurons from becoming inactive, thus addressing the 'dying ReLU' problem in deep networks.\n","---\n","- **Positive Inputs**: Demonstrates linear behavior $f(x) = x$, facilitating unhindered gradient flow.\n","- **Negative Inputs**: Not flat like standard ReLU; has a slight slope, typically 0.01, determined by the $ \\alpha $ parameter. Here, $\\alpha = 0.2$.\n","- **Dead Neuron Mitigation**: The minimal slope ensures neurons remain active.\n","- **Continuity**: Leaky ReLU is continuous, but non-smooth at $ x=0$.\n","- **Extremal Behavior**: As $x$ tends to $ \\pm \\infty $, the function behavior is linear or gradually declining.\n","\n","---"]},{"cell_type":"markdown","id":"c3555ccb","metadata":{"papermill":{"duration":0.025144,"end_time":"2023-07-27T13:37:17.341036","exception":false,"start_time":"2023-07-27T13:37:17.315892","status":"completed"},"tags":[]},"source":["\n","<h1 id=\"credits\" style=\"color: #4CAF50;\">🥇 Credits ⤵︎ </h1>\n","<h4 style=\"color: #2196F3;\">For more information, visit the works of:</h4>\n","<ul style=\"list-style-type: square;\">\n","  <li><a href=\"https://arxiv.org/pdf/2109.14545.pdf\">Dubey, Singh, Chaudhuri. Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark. Neurocomputing, 2021.</a></li>\n","  <li><a href=\"https://github.com/shivram1987/ActivationFunctions\">GitHub: ActivationFunctions</a></li>\n","  <li><a href=\"https://medium.com/geekculture/different-activation-functions-for-deep-neural-networks-you-should-know-ea5e86f51e84\">Medium: Different Activation Functions for Deep Neural Networks</a></li>\n","  <li><a href=\"https://www.kaggle.com/code/mpwolke/nn-activation-functions-choreography\">@mpwolke's NN Activation Functions Choreography notebook 🩰</a></li>\n","  <li><a href=\"https://www.kaggle.com/code/samuelcortinhas/mish-a-self-regularized-activation-function-tf\">@samuelcortinhas's Mish: A Self Regularized Activation Function notebook</a></li>\n","</ul>\n"]},{"cell_type":"markdown","id":"4bf00a5b","metadata":{"papermill":{"duration":0.028656,"end_time":"2023-07-27T13:37:17.395355","exception":false,"start_time":"2023-07-27T13:37:17.366699","status":"completed"},"tags":[]},"source":["<div style=\"background-color: #f2f2f2; padding: 53px; border-radius: 5px;\">\n","  <h3>If you found this notebook helpful...</h3>\n","  <p>\n","  Please consider giving it a star. Your support helps me continue to develop high-quality code and pursue my career as a data analyst/engineer. Feedback is always welcome and appreciated. Thank you for taking the time to read my work! \n","  </p> \n","  <h4>\n","  <p style=\"text-align: right;\">\n","  <a href=\"https://github.com/patmejia\"> - pat [¬º-°]¬ </a>\n","  </h4>\n","  </p>\n","</div>"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"},"papermill":{"default_parameters":{},"duration":64.777511,"end_time":"2023-07-27T13:37:20.108892","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-07-27T13:36:15.331381","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}