my version
This commit is contained in:
oskar
parent
698daaf98a
commit
86a62efd36
570
work-sc.ipynb
570
work-sc.ipynb
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@ -5,19 +5,19 @@
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"id": "initial_id",
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"id": "initial_id",
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"metadata": {
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"metadata": {
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"ExecuteTime": {
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"ExecuteTime": {
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"end_time": "2025-11-04T14:28:00.043927Z",
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"end_time": "2025-11-04T21:43:32.795850Z",
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"start_time": "2025-11-04T14:27:59.939813Z"
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"start_time": "2025-11-04T21:43:32.794457Z"
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}
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}
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},
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},
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"source": "import numpy as np",
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"source": "import numpy as np",
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"outputs": [],
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"outputs": [],
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"execution_count": 3
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"execution_count": 45
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},
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},
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{
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{
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"metadata": {
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"metadata": {
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"ExecuteTime": {
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"ExecuteTime": {
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"end_time": "2025-11-04T14:28:09.512985Z",
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"end_time": "2025-11-04T21:43:32.811210Z",
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"start_time": "2025-11-04T14:28:09.508856Z"
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"start_time": "2025-11-04T21:43:32.809638Z"
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}
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}
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},
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},
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"cell_type": "code",
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"cell_type": "code",
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@ -32,13 +32,13 @@
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],
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],
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"id": "48cafaf4b64967bb",
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"id": "48cafaf4b64967bb",
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"outputs": [],
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"outputs": [],
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"execution_count": 4
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"execution_count": 46
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},
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},
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{
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{
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"metadata": {
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"metadata": {
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"ExecuteTime": {
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"ExecuteTime": {
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"end_time": "2025-11-04T14:28:39.907457Z",
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"end_time": "2025-11-04T21:43:32.862226Z",
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"start_time": "2025-11-04T14:28:39.903244Z"
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"start_time": "2025-11-04T21:43:32.860368Z"
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}
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}
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},
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},
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"cell_type": "code",
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"cell_type": "code",
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@ -62,82 +62,520 @@
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],
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],
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"id": "d13137630b41b756",
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"id": "d13137630b41b756",
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"outputs": [],
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"outputs": [],
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"execution_count": 6
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"execution_count": 47
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},
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},
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{
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{
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"metadata": {
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"metadata": {
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"ExecuteTime": {
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"ExecuteTime": {
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"end_time": "2025-11-04T14:29:00.821197Z",
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"end_time": "2025-11-04T21:46:21.643740Z",
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"start_time": "2025-11-04T14:29:00.795742Z"
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"start_time": "2025-11-04T21:46:21.639693Z"
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}
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}
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},
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},
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"cell_type": "code",
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"cell_type": "code",
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"source": "init_layers(nn_architecture)",
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"source": [
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"params = init_layers(nn_architecture)\n",
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"# params"
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],
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"id": "31f205147667dea6",
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"id": "31f205147667dea6",
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"outputs": [],
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"execution_count": 64
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:43:32.957461Z",
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"start_time": "2025-11-04T21:43:32.955675Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def sigmoid(Z):\n",
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" return 1/(1+np.exp(-Z))\n",
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"\n",
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"def relu(Z):\n",
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" return np.maximum(0,Z)\n",
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"\n",
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"def sigmoid_backward(dA, Z):\n",
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" sig = sigmoid(Z)\n",
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" return dA * sig * (1 - sig)\n",
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"\n",
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"def relu_backward(dA, Z):\n",
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" dZ = np.array(dA, copy = True)\n",
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" dZ[Z <= 0] = 0;\n",
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" return dZ;"
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],
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"id": "c1b960e7dcf09d91",
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"outputs": [],
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"execution_count": 49
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:44:22.909895Z",
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"start_time": "2025-11-04T21:44:22.906363Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def single_layer_forward_propagation(A_prev, W_curr, b_curr, activation=\"relu\"):\n",
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" Z_curr = np.dot(W_curr, A_prev) + b_curr\n",
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"\n",
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" if activation == \"relu\":\n",
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" activation_func = relu\n",
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" elif activation == \"sigmoid\":\n",
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" activation_func = sigmoid\n",
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" else:\n",
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" raise Exception('Non-supported activation function')\n",
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"\n",
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" return activation_func(Z_curr), Z_curr"
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],
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"id": "efae2e184daf2fce",
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"outputs": [],
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"execution_count": 61
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:43:33.055558Z",
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"start_time": "2025-11-04T21:43:33.053594Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def full_forward_propagation(X, params_values, nn_architecture):\n",
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" memory = {}\n",
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" A_curr = X\n",
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"\n",
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" for idx, layer in enumerate(nn_architecture):\n",
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" layer_idx = idx + 1\n",
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" A_prev = A_curr\n",
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"\n",
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" activ_function_curr = layer[\"activation\"]\n",
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" W_curr = params_values[\"W\" + str(layer_idx)]\n",
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" b_curr = params_values[\"b\" + str(layer_idx)]\n",
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" A_curr, Z_curr = single_layer_forward_propagation(A_prev, W_curr, b_curr, activ_function_curr)\n",
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"\n",
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" memory[\"A\" + str(idx)] = A_prev\n",
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" memory[\"Z\" + str(layer_idx)] = Z_curr\n",
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"\n",
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" return A_curr, memory"
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],
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"id": "c3cd9e8f51dbe967",
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"outputs": [],
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"execution_count": 51
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:43:33.103372Z",
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"start_time": "2025-11-04T21:43:33.101510Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def get_cost_value(Y_hat, Y):\n",
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" m = Y_hat.shape[1]\n",
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" cost = -1 / m * (np.dot(Y, np.log(Y_hat).T) + np.dot(1 - Y, np.log(1 - Y_hat).T))\n",
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" return np.squeeze(cost)\n",
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"\n",
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"# an auxiliary function that converts probability into class\n",
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"def convert_prob_into_class(probs):\n",
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" probs_ = np.copy(probs)\n",
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" probs_[probs_ > 0.5] = 1\n",
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" probs_[probs_ <= 0.5] = 0\n",
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" return probs_\n",
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"\n",
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"def get_accuracy_value(Y_hat, Y):\n",
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" Y_hat_ = convert_prob_into_class(Y_hat)\n",
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" return (Y_hat_ == Y).all(axis=0).mean()"
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],
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"id": "121416e7bbab57bb",
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"outputs": [],
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"execution_count": 52
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:43:33.176375Z",
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"start_time": "2025-11-04T21:43:33.169411Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def single_layer_backward_propagation(dA_curr, W_curr, b_curr, Z_curr, A_prev, activation=\"relu\"):\n",
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" m = A_prev.shape[1]\n",
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"\n",
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" if activation is \"relu\":\n",
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" backward_activation_func = relu_backward\n",
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" elif activation is \"sigmoid\":\n",
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" backward_activation_func = sigmoid_backward\n",
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" else:\n",
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" raise Exception('Non-supported activation function')\n",
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"\n",
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" dZ_curr = backward_activation_func(dA_curr, Z_curr)\n",
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" dW_curr = np.dot(dZ_curr, A_prev.T) / m\n",
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" db_curr = np.sum(dZ_curr, axis=1, keepdims=True) / m\n",
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" dA_prev = np.dot(W_curr.T, dZ_curr)\n",
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"\n",
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" return dA_prev, dW_curr, db_curr"
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],
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"id": "92e4b87664f18a63",
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"outputs": [],
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"execution_count": 53
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},
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{
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"metadata": {
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"ExecuteTime": {
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||||||
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"end_time": "2025-11-04T21:43:33.243823Z",
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||||||
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"start_time": "2025-11-04T21:43:33.234283Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def full_backward_propagation(Y_hat, Y, memory, params_values, nn_architecture):\n",
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" grads_values = {}\n",
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" m = Y.shape[1]\n",
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" Y = Y.reshape(Y_hat.shape)\n",
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"\n",
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" dA_prev = - (np.divide(Y, Y_hat) - np.divide(1 - Y, 1 - Y_hat));\n",
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"\n",
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" for layer_idx_prev, layer in reversed(list(enumerate(nn_architecture))):\n",
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" layer_idx_curr = layer_idx_prev + 1\n",
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" activ_function_curr = layer[\"activation\"]\n",
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"\n",
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" dA_curr = dA_prev\n",
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"\n",
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" A_prev = memory[\"A\" + str(layer_idx_prev)]\n",
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" Z_curr = memory[\"Z\" + str(layer_idx_curr)]\n",
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" W_curr = params_values[\"W\" + str(layer_idx_curr)]\n",
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" b_curr = params_values[\"b\" + str(layer_idx_curr)]\n",
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"\n",
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" dA_prev, dW_curr, db_curr = single_layer_backward_propagation(\n",
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" dA_curr, W_curr, b_curr, Z_curr, A_prev, activ_function_curr)\n",
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"\n",
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" grads_values[\"dW\" + str(layer_idx_curr)] = dW_curr\n",
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" grads_values[\"db\" + str(layer_idx_curr)] = db_curr\n",
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"\n",
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" return grads_values"
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],
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"id": "2c8e4eed1846f003",
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"outputs": [],
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"execution_count": 54
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:47:33.615104Z",
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"start_time": "2025-11-04T21:47:33.610483Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def update(params_values, grads_values, nn_architecture, learning_rate):\n",
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" for layer_idx, layer in enumerate(nn_architecture):\n",
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" layer_idx=layer_idx+1\n",
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" params_values[\"W\" + str(layer_idx)] -= learning_rate * grads_values[\"dW\" + str(layer_idx)]\n",
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" params_values[\"b\" + str(layer_idx)] -= learning_rate * grads_values[\"db\" + str(layer_idx)]\n",
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"\n",
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" return params_values;"
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],
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"id": "16320b953a183511",
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"outputs": [],
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"execution_count": 66
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:47:37.239308Z",
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"start_time": "2025-11-04T21:47:37.236527Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def train(X, Y, nn_architecture, epochs, learning_rate, verbose=False, callback=None):\n",
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" # initiation of neural net parameters\n",
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" params_values = init_layers(nn_architecture, 2)\n",
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" # initiation of lists storing the history\n",
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" # of metrics calculated during the learning process\n",
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" cost_history = []\n",
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" accuracy_history = []\n",
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"\n",
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" # performing calculations for subsequent iterations\n",
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" for i in range(epochs):\n",
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" # step forward\n",
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" Y_hat, cashe = full_forward_propagation(X, params_values, nn_architecture)\n",
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"\n",
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" # calculating metrics and saving them in history\n",
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" cost = get_cost_value(Y_hat, Y)\n",
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" cost_history.append(cost)\n",
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" accuracy = get_accuracy_value(Y_hat, Y)\n",
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" accuracy_history.append(accuracy)\n",
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"\n",
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" # step backward - calculating gradient\n",
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" grads_values = full_backward_propagation(Y_hat, Y, cashe, params_values, nn_architecture)\n",
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" # updating model state\n",
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" params_values = update(params_values, grads_values, nn_architecture, learning_rate)\n",
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"\n",
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" if(i % 50 == 0):\n",
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" if(verbose):\n",
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" print(\"Iteration: {:05} - cost: {:.5f} - accuracy: {:.5f}\".format(i, cost, accuracy))\n",
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" if(callback is not None):\n",
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" callback(i, params_values)\n",
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"\n",
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" return params_values"
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],
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"id": "fce33f70bba3898",
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"outputs": [],
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"execution_count": 67
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:43:33.422252Z",
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"start_time": "2025-11-04T21:43:33.417262Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import os\n",
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"import tensorflow as tf\n",
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"\n",
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"from sklearn.datasets import make_moons\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib import cm\n",
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"from mpl_toolkits.mplot3d import Axes3D\n",
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"sns.set_style(\"whitegrid\")\n",
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"\n",
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"import keras\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Dense\n",
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"# from keras.utils import np_utils\n",
|
||||||
|
"from keras import regularizers\n",
|
||||||
|
"\n",
|
||||||
|
"from sklearn.metrics import accuracy_score"
|
||||||
|
],
|
||||||
|
"id": "cccd73b5018799d4",
|
||||||
|
"outputs": [],
|
||||||
|
"execution_count": 57
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-11-04T21:43:33.472509Z",
|
||||||
|
"start_time": "2025-11-04T21:43:33.470657Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"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": 58
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-11-04T21:43:33.520603Z",
|
||||||
|
"start_time": "2025-11-04T21:43:33.518562Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"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": 59
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-11-04T21:51:26.021417Z",
|
||||||
|
"start_time": "2025-11-04T21:51:23.520284Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"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": 77
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-11-04T21:51:27.733451Z",
|
||||||
|
"start_time": "2025-11-04T21:51:27.727264Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"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": 78
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-11-04T21:43:33.666607121Z",
|
||||||
|
"start_time": "2025-11-04T20:21:26.059140Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"cell_type": "code",
|
||||||
|
"source": [
|
||||||
|
"startA = np.random.randn(nn_architecture[0][\"input_dim\"],1) * 0.1\n",
|
||||||
|
"full_forward_propagation(startA, params, nn_architecture)"
|
||||||
|
],
|
||||||
|
"id": "8b672c5fd5832cc",
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"text/plain": [
|
"text/plain": [
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"{'W1': array([[-0.01423588, 0.20572217],\n",
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|
||||||
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|
" {'A0': array([[-0.10166672],\n",
|
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" [-0.01546219, -0.00690309],\n",
|
" [ 0.14706683]]),\n",
|
||||||
" [ 0.07551805, 0.08256466]]),\n",
|
" 'Z1': array([[ 0.0203953 ],\n",
|
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" 'b1': array([[-0.01130692],\n",
|
" [-0.22010647],\n",
|
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" [-0.23678376],\n",
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|
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" [-0.01670494],\n",
|
" [ 0.07300465]]),\n",
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" [ 0.0685398 ]]),\n",
|
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|
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" 'W2': array([[ 0.00235001, 0.04562013, 0.02704928, -0.14350081],\n",
|
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|
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" [ 0.08828171, -0.05800817, -0.05015653, 0.05909533],\n",
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||||||
" [-0.03734863, -0.0461971 , -0.08164661, -0.00451233],\n",
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" 'Z2': array([[-0.18085747],\n",
|
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" [ 0.01213278, 0.09259528, -0.05738197, 0.00527031],\n",
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|
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" 'b2': array([[-0.17042917],\n",
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" 'W3': array([[ 0.17634779, -0.11216078, -0.11919342, 0.05527319, -0.08159809,\n",
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|
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|
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|
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|
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|
" [0.02613133]]),\n",
|
||||||
" [-0.02790857],\n",
|
" 'Z4': array([[-0.09066425],\n",
|
||||||
" [ 0.13966199],\n",
|
" [ 0.05792425],\n",
|
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" [ 0.00322194]]),\n",
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|
||||||
" 'W4': array([[-0.26136608, -0.10015776, -0.0567511 , -0.0225658 , 0.09380238,\n",
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|
||||||
" 0.08367841],\n",
|
" 'A4': array([[0. ],\n",
|
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" [0.05792425],\n",
|
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|
" [0.07822296],\n",
|
||||||
" [ 0.15526339, 0.00936234, 0.02948258, 0.14854308, -0.10868852,\n",
|
" [0.07317913]]),\n",
|
||||||
" 0.08211628],\n",
|
" 'Z5': array([[0.06434517]])})"
|
||||||
" [-0.07879492, 0.15938117, 0.14059044, 0.16447566, 0.15415987,\n",
|
|
||||||
" 0.08406076]]),\n",
|
|
||||||
" 'b4': array([[-0.10230944],\n",
|
|
||||||
" [ 0.04947723],\n",
|
|
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" [ 0.08957326],\n",
|
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" [ 0.0477352 ]]),\n",
|
|
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" 'W5': array([[-0.01145305, 0.01568974, 0.03875967, -0.10262266]]),\n",
|
|
||||||
" 'b5': array([[0.06791429]])}"
|
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 7,
|
"execution_count": 24,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "execute_result"
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"execution_count": 7
|
"execution_count": 24
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-11-04T21:52:07.296371Z",
|
||||||
|
"start_time": "2025-11-04T21:52:01.384867Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"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",
|
||||||
|
"2025-11-04 22:52:01.409083: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW\n",
|
||||||
|
"2025-11-04 22:52:01.409097: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:171] verbose logging is disabled. Rerun with verbose logging (usually --v=1 or --vmodule=cuda_diagnostics=1) to get more diagnostic output from this module\n",
|
||||||
|
"2025-11-04 22:52:01.409099: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:176] retrieving CUDA diagnostic information for host: solaria\n",
|
||||||
|
"2025-11-04 22:52:01.409101: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:183] hostname: solaria\n",
|
||||||
|
"2025-11-04 22:52:01.409176: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] libcuda reported version is: 580.95.5\n",
|
||||||
|
"2025-11-04 22:52:01.409184: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:194] kernel reported version is: 570.195.3\n",
|
||||||
|
"2025-11-04 22:52:01.409185: E external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:287] kernel version 570.195.3 does not match DSO version 580.95.5 -- cannot find working devices in this configuration\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"execution_count": 79
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"ExecuteTime": {
|
||||||
|
"end_time": "2025-11-04T21:53:11.479872Z",
|
||||||
|
"start_time": "2025-11-04T21:53:11.455625Z"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"cell_type": "code",
|
||||||
|
"source": [
|
||||||
|
"Y_test_hat = model.predict_classes(X_test)\n",
|
||||||
|
"acc_test = accuracy_score(y_test, Y_test_hat)\n",
|
||||||
|
"print(\"Test set accuracy: {:.2f} - Goliath\".format(acc_test))"
|
||||||
|
],
|
||||||
|
"id": "ef52bee9c93081d3",
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"ename": "AttributeError",
|
||||||
|
"evalue": "'Sequential' object has no attribute 'predict_classes'",
|
||||||
|
"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[83]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m Y_test_hat = \u001B[43mmodel\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpredict_classes\u001B[49m(X_test)\n\u001B[32m 2\u001B[39m acc_test = accuracy_score(y_test, Y_test_hat)\n\u001B[32m 3\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[33mTest set accuracy: \u001B[39m\u001B[38;5;132;01m{:.2f}\u001B[39;00m\u001B[33m - Goliath\u001B[39m\u001B[33m\"\u001B[39m.format(acc_test))\n",
|
||||||
|
"\u001B[31mAttributeError\u001B[39m: 'Sequential' object has no attribute 'predict_classes'"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"execution_count": 83
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
|
|
|
||||||
Loading…
Reference in a new issue