{ "cells": [ { "cell_type": "code", "id": "initial_id", "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:00.816816Z", "start_time": "2025-11-04T22:00:00.813630Z" } }, "source": "import numpy as np", "outputs": [], "execution_count": 84 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:00.830847Z", "start_time": "2025-11-04T22:00:00.829329Z" } }, "cell_type": "code", "source": [ "nn_architecture = [\n", " {\"input_dim\": 2, \"output_dim\": 4, \"activation\": \"relu\"},\n", " {\"input_dim\": 4, \"output_dim\": 6, \"activation\": \"relu\"},\n", " {\"input_dim\": 6, \"output_dim\": 6, \"activation\": \"relu\"},\n", " {\"input_dim\": 6, \"output_dim\": 4, \"activation\": \"relu\"},\n", " {\"input_dim\": 4, \"output_dim\": 1, \"activation\": \"sigmoid\"},\n", "]" ], "id": "48cafaf4b64967bb", "outputs": [], "execution_count": 85 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:00.885348Z", "start_time": "2025-11-04T22:00:00.880961Z" } }, "cell_type": "code", "source": [ "def init_layers(nn_architecture, seed = 99):\n", " np.random.seed(seed)\n", " number_of_layers = len(nn_architecture)\n", " params_values = {}\n", "\n", " for idx, layer in enumerate(nn_architecture):\n", " layer_idx = idx + 1\n", " layer_input_size = layer[\"input_dim\"]\n", " layer_output_size = layer[\"output_dim\"]\n", "\n", " params_values['W' + str(layer_idx)] = np.random.randn(\n", " layer_output_size, layer_input_size) * 0.1\n", " params_values['b' + str(layer_idx)] = np.random.randn(\n", " layer_output_size, 1) * 0.1\n", "\n", " return params_values\n" ], "id": "d13137630b41b756", "outputs": [], "execution_count": 86 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:00.944688Z", "start_time": "2025-11-04T22:00:00.939752Z" } }, "cell_type": "code", "source": [ "params = init_layers(nn_architecture)\n", "# params" ], "id": "31f205147667dea6", "outputs": [], "execution_count": 87 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:00.994063Z", "start_time": "2025-11-04T22:00:00.990969Z" } }, "cell_type": "code", "source": [ "def sigmoid(Z):\n", " return 1/(1+np.exp(-Z))\n", "\n", "def relu(Z):\n", " return np.maximum(0,Z)\n", "\n", "def sigmoid_backward(dA, Z):\n", " sig = sigmoid(Z)\n", " return dA * sig * (1 - sig)\n", "\n", "def relu_backward(dA, Z):\n", " dZ = np.array(dA, copy = True)\n", " dZ[Z <= 0] = 0;\n", " return dZ;" ], "id": "c1b960e7dcf09d91", "outputs": [], "execution_count": 88 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:01.051837Z", "start_time": "2025-11-04T22:00:01.046197Z" } }, "cell_type": "code", "source": [ "def single_layer_forward_propagation(A_prev, W_curr, b_curr, activation=\"relu\"):\n", " Z_curr = np.dot(W_curr, A_prev) + b_curr\n", "\n", " if activation == \"relu\":\n", " activation_func = relu\n", " elif activation == \"sigmoid\":\n", " activation_func = sigmoid\n", " else:\n", " raise Exception('Non-supported activation function')\n", "\n", " return activation_func(Z_curr), Z_curr" ], "id": "efae2e184daf2fce", "outputs": [], "execution_count": 89 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:01.101365Z", "start_time": "2025-11-04T22:00:01.097608Z" } }, "cell_type": "code", "source": [ "def full_forward_propagation(X, params_values, nn_architecture):\n", " memory = {}\n", " A_curr = X\n", "\n", " for idx, layer in enumerate(nn_architecture):\n", " layer_idx = idx + 1\n", " A_prev = A_curr\n", "\n", " activ_function_curr = layer[\"activation\"]\n", " W_curr = params_values[\"W\" + str(layer_idx)]\n", " b_curr = params_values[\"b\" + str(layer_idx)]\n", " A_curr, Z_curr = single_layer_forward_propagation(A_prev, W_curr, b_curr, activ_function_curr)\n", "\n", " memory[\"A\" + str(idx)] = A_prev\n", " memory[\"Z\" + str(layer_idx)] = Z_curr\n", "\n", " return A_curr, memory" ], "id": "c3cd9e8f51dbe967", "outputs": [], "execution_count": 90 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:01.147862Z", "start_time": "2025-11-04T22:00:01.146127Z" } }, "cell_type": "code", "source": [ "def get_cost_value(Y_hat, Y):\n", " m = Y_hat.shape[1]\n", " cost = -1 / m * (np.dot(Y, np.log(Y_hat).T) + np.dot(1 - Y, np.log(1 - Y_hat).T))\n", " return np.squeeze(cost)\n", "\n", "# an auxiliary function that converts probability into class\n", "def convert_prob_into_class(probs):\n", " probs_ = np.copy(probs)\n", " probs_[probs_ > 0.5] = 1\n", " probs_[probs_ <= 0.5] = 0\n", " return probs_\n", "\n", "def get_accuracy_value(Y_hat, Y):\n", " Y_hat_ = convert_prob_into_class(Y_hat)\n", " return (Y_hat_ == Y).all(axis=0).mean()" ], "id": "121416e7bbab57bb", "outputs": [], "execution_count": 91 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:01.200653Z", "start_time": "2025-11-04T22:00:01.198951Z" } }, "cell_type": "code", "source": [ "def single_layer_backward_propagation(dA_curr, W_curr, b_curr, Z_curr, A_prev, activation=\"relu\"):\n", " m = A_prev.shape[1]\n", "\n", " if activation == \"relu\":\n", " backward_activation_func = relu_backward\n", " elif activation == \"sigmoid\":\n", " backward_activation_func = sigmoid_backward\n", " else:\n", " raise Exception('Non-supported activation function')\n", "\n", " dZ_curr = backward_activation_func(dA_curr, Z_curr)\n", " dW_curr = np.dot(dZ_curr, A_prev.T) / m\n", " db_curr = np.sum(dZ_curr, axis=1, keepdims=True) / m\n", " dA_prev = np.dot(W_curr.T, dZ_curr)\n", "\n", " return dA_prev, dW_curr, db_curr" ], "id": "92e4b87664f18a63", "outputs": [], "execution_count": 92 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:01.259385Z", "start_time": "2025-11-04T22:00:01.253050Z" } }, "cell_type": "code", "source": [ "def full_backward_propagation(Y_hat, Y, memory, params_values, nn_architecture):\n", " grads_values = {}\n", " m = Y.shape[1]\n", " Y = Y.reshape(Y_hat.shape)\n", "\n", " dA_prev = - (np.divide(Y, Y_hat) - np.divide(1 - Y, 1 - Y_hat));\n", "\n", " for layer_idx_prev, layer in reversed(list(enumerate(nn_architecture))):\n", " layer_idx_curr = layer_idx_prev + 1\n", " activ_function_curr = layer[\"activation\"]\n", "\n", " dA_curr = dA_prev\n", "\n", " A_prev = memory[\"A\" + str(layer_idx_prev)]\n", " Z_curr = memory[\"Z\" + str(layer_idx_curr)]\n", " W_curr = params_values[\"W\" + str(layer_idx_curr)]\n", " b_curr = params_values[\"b\" + str(layer_idx_curr)]\n", "\n", " dA_prev, dW_curr, db_curr = single_layer_backward_propagation(\n", " dA_curr, W_curr, b_curr, Z_curr, A_prev, activ_function_curr)\n", "\n", " grads_values[\"dW\" + str(layer_idx_curr)] = dW_curr\n", " grads_values[\"db\" + str(layer_idx_curr)] = db_curr\n", "\n", " return grads_values" ], "id": "2c8e4eed1846f003", "outputs": [], "execution_count": 93 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:01.319868Z", "start_time": "2025-11-04T22:00:01.312729Z" } }, "cell_type": "code", "source": [ "def update(params_values, grads_values, nn_architecture, learning_rate):\n", " for layer_idx, layer in enumerate(nn_architecture):\n", " layer_idx=layer_idx+1\n", " params_values[\"W\" + str(layer_idx)] -= learning_rate * grads_values[\"dW\" + str(layer_idx)]\n", " params_values[\"b\" + str(layer_idx)] -= learning_rate * grads_values[\"db\" + str(layer_idx)]\n", "\n", " return params_values;" ], "id": "16320b953a183511", "outputs": [], "execution_count": 94 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:01.380430Z", "start_time": "2025-11-04T22:00:01.373966Z" } }, "cell_type": "code", "source": [ "def train(X, Y, nn_architecture, epochs, learning_rate, verbose=False, callback=None):\n", " # initiation of neural net parameters\n", " params_values = init_layers(nn_architecture, 2)\n", " # initiation of lists storing the history\n", " # of metrics calculated during the learning process\n", " cost_history = []\n", " accuracy_history = []\n", "\n", " # performing calculations for subsequent iterations\n", " for i in range(epochs):\n", " # step forward\n", " Y_hat, cashe = full_forward_propagation(X, params_values, nn_architecture)\n", "\n", " # calculating metrics and saving them in history\n", " cost = get_cost_value(Y_hat, Y)\n", " cost_history.append(cost)\n", " accuracy = get_accuracy_value(Y_hat, Y)\n", " accuracy_history.append(accuracy)\n", "\n", " # step backward - calculating gradient\n", " grads_values = full_backward_propagation(Y_hat, Y, cashe, params_values, nn_architecture)\n", " # updating model state\n", " params_values = update(params_values, grads_values, nn_architecture, learning_rate)\n", "\n", " if(i % 50 == 0):\n", " if(verbose):\n", " print(\"Iteration: {:05} - cost: {:.5f} - accuracy: {:.5f}\".format(i, cost, accuracy))\n", " if(callback is not None):\n", " callback(i, params_values)\n", "\n", " return params_values" ], "id": "fce33f70bba3898", "outputs": [], "execution_count": 95 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:01.444163Z", "start_time": "2025-11-04T22:00:01.436199Z" } }, "cell_type": "code", "source": [ "import os\n", "import tensorflow as tf\n", "\n", "from sklearn.datasets import make_moons\n", "from sklearn.model_selection import train_test_split\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from matplotlib import cm\n", "from mpl_toolkits.mplot3d import Axes3D\n", "sns.set_style(\"whitegrid\")\n", "\n", "import keras\n", "from keras.models import Sequential\n", "from keras.layers import Dense\n", "# from keras.utils import np_utils\n", "from keras import regularizers\n", "\n", "from sklearn.metrics import accuracy_score" ], "id": "cccd73b5018799d4", "outputs": [], "execution_count": 96 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:01.500700Z", "start_time": "2025-11-04T22:00:01.497537Z" } }, "cell_type": "code", "source": [ "# number of samples in the data set\n", "N_SAMPLES = 1000\n", "# ratio between training and test sets\n", "TEST_SIZE = 0.1" ], "id": "4f66ffa878f01c02", "outputs": [], "execution_count": 97 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:01.560294Z", "start_time": "2025-11-04T22:00:01.553505Z" } }, "cell_type": "code", "source": [ "X, y = make_moons(n_samples = N_SAMPLES, noise=0.2, random_state=100)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=42)" ], "id": "bebe0ed00a2d514", "outputs": [], "execution_count": 98 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:04.165839Z", "start_time": "2025-11-04T22:00:01.614181Z" } }, "cell_type": "code", "source": [ "params_values = train(np.transpose(X_train), np.transpose(y_train.reshape((y_train.shape[0], 1))), nn_architecture, 20000, 0.01)\n", "# params_values\n" ], "id": "ce04892d496c5147", "outputs": [], "execution_count": 99 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:11.428146Z", "start_time": "2025-11-04T22:00:11.422370Z" } }, "cell_type": "code", "source": [ "Y_test_hat, _ = full_forward_propagation(np.transpose(X_test), params_values, nn_architecture)\n", "\n", "acc_test = get_accuracy_value(Y_test_hat, np.transpose(y_test.reshape((y_test.shape[0], 1))))\n", "print(\"Test set accuracy: {:.2f} - David\".format(acc_test))\n" ], "id": "26e7a2a8848714d9", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test set accuracy: 0.46 - David\n" ] } ], "execution_count": 105 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:29.176357Z", "start_time": "2025-11-04T22:00:23.282276Z" } }, "cell_type": "code", "source": [ "model = Sequential()\n", "model.add(Dense(25, input_dim=2,activation='relu'))\n", "model.add(Dense(50, activation='relu'))\n", "model.add(Dense(50, activation='relu'))\n", "model.add(Dense(25, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "\n", "model.compile(loss='binary_crossentropy', optimizer=\"sgd\", metrics=['accuracy'])\n", "\n", "# Training\n", "history = model.fit(X_train, y_train, epochs=200, verbose=0)" ], "id": "f05ff40ed26e45c2", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/oskar/projects/nn-from-scratch/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:95: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] } ], "execution_count": 106 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:00:33.380478Z", "start_time": "2025-11-04T22:00:33.309269Z" } }, "cell_type": "code", "source": [ "Y_test_prob = model.predict(X_test)\n", "Y_test_hat = (Y_test_prob > 0.5).astype(int).ravel()\n", "acc_test = accuracy_score(y_test, Y_test_hat)\n", "print(\"Test set accuracy: {:.2f} - Goliath\".format(acc_test))" ], "id": "ef52bee9c93081d3", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:6 out of the last 10 calls to .one_step_on_data_distributed at 0x7e21f476c900> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step \n", "Test set accuracy: 0.99 - Goliath\n" ] } ], "execution_count": 107 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:03:33.972219Z", "start_time": "2025-11-04T22:03:33.966407Z" } }, "cell_type": "code", "source": [ "def make_plot(X, y, plot_name, file_name=None, XX=None, YY=None, preds=None, dark=False):\n", " if (dark):\n", " plt.style.use('dark_background')\n", " else:\n", " sns.set_style(\"whitegrid\")\n", " plt.figure(figsize=(16,12))\n", " axes = plt.gca()\n", " axes.set(xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n", " plt.title(plot_name, fontsize=30)\n", " plt.subplots_adjust(left=0.20)\n", " plt.subplots_adjust(right=0.80)\n", " if(XX is not None and YY is not None and preds is not None):\n", " plt.contourf(XX, YY, preds.reshape(XX.shape), 25, alpha = 1, cmap=cm.Spectral)\n", " plt.contour(XX, YY, preds.reshape(XX.shape), levels=[.5], cmap=\"Greys\", vmin=0, vmax=.6)\n", " plt.scatter(X[:, 0], X[:, 1], c=y.ravel(), s=40, cmap=plt.cm.Spectral, edgecolors='black')\n", " if(file_name):\n", " plt.savefig(file_name)\n", " plt.close()" ], "id": "9535365d1da72395", "outputs": [], "execution_count": 109 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:02:51.938430Z", "start_time": "2025-11-04T22:02:51.934316Z" } }, "cell_type": "code", "source": [ "# boundary of the graph\n", "GRID_X_START = -1.5\n", "GRID_X_END = 2.5\n", "GRID_Y_START = -1.0\n", "GRID_Y_END = 2\n", "# output directory (the folder must be created on the drive)\n", "OUTPUT_DIR = \"./binary_classification_vizualizations/\"\n", "### Definition of grid boundaries\n", "grid = np.mgrid[GRID_X_START:GRID_X_END:100j, GRID_X_START:GRID_Y_END:100j]\n", "grid_2d = grid.reshape(2, -1).T\n", "XX, YY = grid" ], "id": "b070f03d55981894", "outputs": [], "execution_count": 108 }, { "metadata": { "ExecuteTime": { "end_time": "2025-11-04T22:05:02.290039Z", "start_time": "2025-11-04T22:05:02.042691Z" } }, "cell_type": "code", "source": [ "def callback_keras_plot(epoch, logs):\n", " plot_title = \"Keras Model - It: {:05}\".format(epoch)\n", " file_name = \"keras_model_{:05}.png\".format(epoch)\n", " file_path = os.path.join(OUTPUT_DIR, file_name)\n", " prediction_probs = model.predict_proba(grid_2d, batch_size=32, verbose=0)\n", " make_plot(X_test, y_test, plot_title, file_name=file_path, XX=XX, YY=YY, preds=prediction_probs)\n", "\n", "\n", "# Adding callback functions that they will run in every epoch\n", "testmodelcb = keras.callbacks.LambdaCallback(on_epoch_end=callback_keras_plot)\n", "\n", "# Building a model\n", "model = Sequential()\n", "model.add(Dense(25, input_dim=2, activation='relu'))\n", "model.add(Dense(50, activation='relu'))\n", "model.add(Dense(50, activation='relu'))\n", "model.add(Dense(25, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "\n", "model.compile(loss='binary_crossentropy', optimizer=\"sgd\", metrics=['accuracy'])\n", "\n", "# Training\n", "history = model.fit(X_train, y_train, epochs=200, verbose=0, callbacks=[testmodelcb])\n", "rediction_probs = model.predict_proba(grid_2d, batch_size=32, verbose=0)\n", "make_plot(X_test, y_test, \"Keras Model\", file_name=None, XX=XX, YY=YY, preds=prediction_probs)" ], "id": "6feab7da06e7a828", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/oskar/projects/nn-from-scratch/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:95: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] }, { "ename": "AttributeError", "evalue": "'Sequential' object has no attribute 'predict_proba'", "output_type": "error", "traceback": [ "\u001B[31m---------------------------------------------------------------------------\u001B[39m", "\u001B[31mAttributeError\u001B[39m Traceback (most recent call last)", "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[110]\u001B[39m\u001B[32m, line 23\u001B[39m\n\u001B[32m 20\u001B[39m model.compile(loss=\u001B[33m'\u001B[39m\u001B[33mbinary_crossentropy\u001B[39m\u001B[33m'\u001B[39m, optimizer=\u001B[33m\"\u001B[39m\u001B[33msgd\u001B[39m\u001B[33m\"\u001B[39m, metrics=[\u001B[33m'\u001B[39m\u001B[33maccuracy\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m 22\u001B[39m \u001B[38;5;66;03m# Training\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m23\u001B[39m history = \u001B[43mmodel\u001B[49m\u001B[43m.\u001B[49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m200\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43mtestmodelcb\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 24\u001B[39m rediction_probs = model.predict_proba(grid_2d, batch_size=\u001B[32m32\u001B[39m, verbose=\u001B[32m0\u001B[39m)\n\u001B[32m 25\u001B[39m make_plot(X_test, y_test, \u001B[33m\"\u001B[39m\u001B[33mKeras Model\u001B[39m\u001B[33m\"\u001B[39m, file_name=\u001B[38;5;28;01mNone\u001B[39;00m, XX=XX, YY=YY, preds=prediction_probs)\n", "\u001B[36mFile \u001B[39m\u001B[32m~/projects/nn-from-scratch/.venv/lib/python3.13/site-packages/keras/src/utils/traceback_utils.py:122\u001B[39m, in \u001B[36mfilter_traceback..error_handler\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 119\u001B[39m filtered_tb = _process_traceback_frames(e.__traceback__)\n\u001B[32m 120\u001B[39m \u001B[38;5;66;03m# To get the full stack trace, call:\u001B[39;00m\n\u001B[32m 121\u001B[39m \u001B[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m122\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m e.with_traceback(filtered_tb) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m 123\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m 124\u001B[39m \u001B[38;5;28;01mdel\u001B[39;00m filtered_tb\n", "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[110]\u001B[39m\u001B[32m, line 5\u001B[39m, in \u001B[36mcallback_keras_plot\u001B[39m\u001B[34m(epoch, logs)\u001B[39m\n\u001B[32m 3\u001B[39m file_name = \u001B[33m\"\u001B[39m\u001B[33mkeras_model_\u001B[39m\u001B[38;5;132;01m{:05}\u001B[39;00m\u001B[33m.png\u001B[39m\u001B[33m\"\u001B[39m.format(epoch)\n\u001B[32m 4\u001B[39m file_path = os.path.join(OUTPUT_DIR, file_name)\n\u001B[32m----> \u001B[39m\u001B[32m5\u001B[39m prediction_probs = \u001B[43mmodel\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpredict_proba\u001B[49m(grid_2d, batch_size=\u001B[32m32\u001B[39m, verbose=\u001B[32m0\u001B[39m)\n\u001B[32m 6\u001B[39m make_plot(X_test, y_test, plot_title, file_name=file_path, XX=XX, YY=YY, preds=prediction_probs)\n", "\u001B[31mAttributeError\u001B[39m: 'Sequential' object has no attribute 'predict_proba'" ] } ], "execution_count": 110 } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }