diff --git a/.buildinfo b/.buildinfo index e369f0eb..690c78ed 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: af2f8f4e454a9fab8caa1368ea3e6801 +config: ba8ea9ec9ec10ef9096dd3afae52f083 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/.doctrees/api-reference.doctree b/.doctrees/api-reference.doctree index 99d1a173..ab7a71a2 100644 Binary files a/.doctrees/api-reference.doctree and b/.doctrees/api-reference.doctree differ diff --git a/.doctrees/basic-usage.doctree b/.doctrees/basic-usage.doctree index 76a1090a..ebdaa15e 100644 Binary files a/.doctrees/basic-usage.doctree and b/.doctrees/basic-usage.doctree differ diff --git a/.doctrees/changelog.doctree b/.doctrees/changelog.doctree index f56f665b..b921f598 100644 Binary files a/.doctrees/changelog.doctree and b/.doctrees/changelog.doctree differ diff --git a/.doctrees/citation.doctree b/.doctrees/citation.doctree index 1fe7333d..65f27388 100644 Binary files a/.doctrees/citation.doctree and b/.doctrees/citation.doctree differ diff --git a/.doctrees/contributing.doctree 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b/.doctrees/index.doctree index a7e9124c..6e222914 100644 Binary files a/.doctrees/index.doctree and b/.doctrees/index.doctree differ diff --git a/.doctrees/license.doctree b/.doctrees/license.doctree index e82cfad6..4b522c5a 100644 Binary files a/.doctrees/license.doctree and b/.doctrees/license.doctree differ diff --git a/.doctrees/nbsphinx/examples/psMNIST.ipynb b/.doctrees/nbsphinx/examples/psMNIST.ipynb index 0d756021..cd066b33 100644 --- a/.doctrees/nbsphinx/examples/psMNIST.ipynb +++ b/.doctrees/nbsphinx/examples/psMNIST.ipynb @@ -39,10 +39,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:27.475964Z", - "iopub.status.busy": "2023-07-20T21:47:27.475467Z", - "iopub.status.idle": "2023-07-20T21:47:29.833561Z", - "shell.execute_reply": "2023-07-20T21:47:29.832831Z" + "iopub.execute_input": "2024-06-19T15:53:49.995468Z", + "iopub.status.busy": "2024-06-19T15:53:49.995245Z", + "iopub.status.idle": "2024-06-19T15:53:51.697031Z", + "shell.execute_reply": "2024-06-19T15:53:51.696621Z" } }, "outputs": [ @@ -50,14 +50,24 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-07-20 21:47:27.927512: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + "2024-06-19 15:53:50.513017: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-06-19 15:53:50.515207: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-06-19 15:53:50.541499: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-06-19 15:53:51.061982: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], "source": [ "%matplotlib inline\n", "\n", + "import keras\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from IPython.display import Image, display\n", @@ -87,10 +97,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:29.837478Z", - "iopub.status.busy": "2023-07-20T21:47:29.837040Z", - "iopub.status.idle": "2023-07-20T21:47:29.841472Z", - "shell.execute_reply": "2023-07-20T21:47:29.840864Z" + "iopub.execute_input": "2024-06-19T15:53:51.700201Z", + "iopub.status.busy": "2024-06-19T15:53:51.699959Z", + "iopub.status.idle": "2024-06-19T15:53:51.702507Z", + "shell.execute_reply": "2024-06-19T15:53:51.702238Z" } }, "outputs": [], @@ -105,7 +115,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now obtain the standard MNIST dataset of handwritten digits from `tf.keras.datasets`." + "We now obtain the standard MNIST dataset of handwritten digits from `keras.datasets`." ] }, { @@ -113,10 +123,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:29.844053Z", - "iopub.status.busy": "2023-07-20T21:47:29.843780Z", - "iopub.status.idle": "2023-07-20T21:47:30.739177Z", - "shell.execute_reply": "2023-07-20T21:47:30.738443Z" + "iopub.execute_input": "2024-06-19T15:53:51.704720Z", + "iopub.status.busy": "2024-06-19T15:53:51.704469Z", + "iopub.status.idle": "2024-06-19T15:53:51.826178Z", + "shell.execute_reply": "2024-06-19T15:53:51.825750Z" } }, "outputs": [], @@ -124,7 +134,7 @@ "(train_images, train_labels), (\n", " test_images,\n", " test_labels,\n", - ") = tf.keras.datasets.mnist.load_data()" + ") = keras.datasets.mnist.load_data()" ] }, { @@ -141,16 +151,16 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:30.742900Z", - "iopub.status.busy": "2023-07-20T21:47:30.742508Z", - "iopub.status.idle": "2023-07-20T21:47:31.026957Z", - "shell.execute_reply": "2023-07-20T21:47:31.026295Z" + "iopub.execute_input": "2024-06-19T15:53:51.828606Z", + "iopub.status.busy": "2024-06-19T15:53:51.828434Z", + "iopub.status.idle": "2024-06-19T15:53:51.990916Z", + "shell.execute_reply": "2024-06-19T15:53:51.990606Z" } }, "outputs": [ { "data": { - "image/png": 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lpeUe88TLNZ1zbv/+/W7UqFEuPj7ehYeHuwYNGrjU1FQ3b9680z4flV2S+sknnwSsV3Zp4omXx5647yd66aWX3DXXXOMiIyNdcnKyy87OrvDSxqKiIjdq1Ch3+eWXu9q1a7u+ffu6HTt2OElu8uTJ1XaMP/74o3v88cddkyZNXHh4uIuPj3d/+MMf3NGjR097LJUpLCx0AwcOdHFxcU6SXfJZdknq66+/HrB+2ff9xMs9nXNuy5Yt7o477nBXXHGFi4yMdImJiW7AgAHuH//4x2n3IS8vz/Xp08cu57z//vvd6tWrT3tJqnPOHThwwA0cONDVqVPHxcbGuszMTPfhhx86Se7VV1+19Sr6vv373/92t956q4uKinKSPF+eOmHCBNe2bVsXGxvrwsPDXUJCgrv33nvdN9984+lxUDGfc9XwiSVqVGZmpt54440K30a41GzdulU33HCD/vrXv2rQoEE1vTuXlDfffFP9+vXTBx98oJtuuqmmdwdB4jMFXLCOHDlSbtnMmTMVEhKiW2+9tQb26NJx8nNfWlqq2bNnq27duvr5z39eQ3uF6sBnCrhgTZkyRZs2bVLXrl0VFhamVatWadWqVbrnnnvK/T4EqteYMWN05MgRderUSceOHdOSJUu0fv16TZo0KeCXD3HhIQq4YHXu3Fl///vf9eSTT6qwsFAJCQl67LHHNGHChJretYtet27dNH36dK1YsUJHjx5V8+bNNXv27IDfqseFic8UAACGzxQAAIYoAABMlT9T4H++DQAXtqp8WsCZAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAhNX0DgCnExoa6nkmNjb2LOxJ9Rg9enRQc9HR0Z5nWrRo4Xlm1KhRnmemTZvmeebOO+/0PCNJR48e9TwzefJkzzOPP/6455mLAWcKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYboh3kUlISPA8ExER4Xmmc+fOnmduvvlmzzOSFBcX53mmf//+QW3rYrNnzx7PM7NmzfI8069fP88zP/zwg+cZSdq2bZvnmXfffTeobV2KOFMAABiiAAAwRAEAYIgCAMAQBQCAIQoAAEMUAACGKAAADFEAABiiAAAwRAEAYIgCAMD4nHOuSiv6fGd7X3CCtm3bBjWXk5PjeSY2NjaobeHc8vv9nmfuvvtuzzOFhYWeZ4Kxb9++oOYOHz7seWbHjh1BbetiU5Uf95wpAAAMUQAAGKIAADBEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwHCX1PPU5ZdfHtTchg0bPM80bdo0qG1dbIJ57vLz8z3PdO3a1fOMJJWUlHie4Q64OBF3SQUAeEIUAACGKAAADFEAABiiAAAwRAEAYIgCAMAQBQCAIQoAAEMUAACGKAAADFEAAJiwmt4BVOzQoUNBzT388MOeZ26//XbPM1u2bPE8M2vWLM8zwdq6davnme7du3ueKSoq8jzTqlUrzzOSdP/99wc1B3jBmQIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwBAFAIAhCgAAQxQAAMbnnHNVWtHnO9v7ghpSt25dzzM//PCD55m5c+d6npGkYcOGeZ4ZPHiw55m//e1vnmeAC0lVftxzpgAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgAmr6R1Azfv+++/PyXYKCgrOyXYkacSIEZ5nFi1a5HnG7/d7ngHOZ5wpAAAMUQAAGKIAADBEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwPicc65KK/p8Z3tfcJGLiYkJam758uWeZ7p06eJ5pmfPnp5n3nnnHc8zQE2pyo97zhQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADDcEA/nvWbNmnme2bx5s+eZ/Px8zzO5ubmeZz799FPPM5L07LPPep6p4n/euERwQzwAgCdEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwBAFAIDhhni4KPXr18/zTHZ2tueZOnXqeJ4J1vjx4z3P/OUvf/E8s2/fPs8zuDBwQzwAgCdEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwBAFAIDhhnjA/9e6dWvPMzNmzPA8k5qa6nkmWHPnzvU8M3HiRM8ze/fu9TyDc48b4gEAPCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAw3xAPOQFxcnOeZ3r17B7Wt7OxszzPB/Hebk5PjeaZ79+6eZ3DucUM8AIAnRAEAYIgCAMAQBQCAIQoAAEMUAACGKAAADFEAABiiAAAwRAEAYIgCAMAQBQCAIQoAAMNdUoELxLFjxzzPhIWFeZ45fvy455m0tDTPM+vWrfM8gzPDXVIBAJ4QBQCAIQoAAEMUAACGKAAADFEAABiiAAAwRAEAYIgCAMAQBQCAIQoAAEMUAADG+92ygItUmzZtPM+kp6d7nmnfvr3nGSm4m9sF4/PPP/c88957752FPUFN4EwBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAADDDfFw3mvRooXnmdGjR3ueueOOOzzPNGjQwPPMuVRaWup5Zt++fZ5n/H6/5xmcnzhTAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAcEM8BCWYG8HdeeedQW0rmJvbJSUlBbWt89mnn37qeWbixImeZ5YtW+Z5BhcPzhQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADDcEO8ic9VVV3meadmypeeZOXPmeJ5JTk72PHO+27Bhg+eZqVOnBrWtt956y/OM3+8Palu4dHGmAAAwRAEAYIgCAMAQBQCAIQoAAEMUAACGKAAADFEAABiiAAAwRAEAYIgCAMAQBQCAIQoAAMNdUs+Byy+/3PPM3Llzg9pW27ZtPc80bdo0qG2dz9avX+95Zvr06Z5n1qxZ43nmyJEjnmeAc4UzBQCAIQoAAEMUAACGKAAADFEAABiiAAAwRAEAYIgCAMAQBQCAIQoAAEMUAACGKAAAzCV9Q7yOHTt6nnn44Yc9z3To0MHzTOPGjT3PnO+Ki4uDmps1a5bnmUmTJnmeKSoq8jwDXGw4UwAAGKIAADBEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwFzSN8Tr16/fOZk5lz7//HPPMytWrPA8c/z4cc8z06dP9zwjSfn5+UHNAfCOMwUAgCEKAABDFAAAhigAAAxRAAAYogAAMEQBAGCIAgDAEAUAgCEKAABDFAAAhigAAIzPOeeqtKLPd7b3BQBwFlXlxz1nCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwBAFAIAhCgAAQxQAAIYoAAAMUQAAGKIAADBEAQBgiAIAwIRVdUXn3NncDwDAeYAzBQCAIQoAAEMUAACGKAAADFEAABiiAAAwRAEAYIgCAMAQBQCA+X9v0jdfJV+L7QAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -192,16 +202,16 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:31.029810Z", - "iopub.status.busy": "2023-07-20T21:47:31.029521Z", - "iopub.status.idle": "2023-07-20T21:47:31.070798Z", - "shell.execute_reply": "2023-07-20T21:47:31.070178Z" + "iopub.execute_input": "2024-06-19T15:53:51.992633Z", + "iopub.status.busy": "2024-06-19T15:53:51.992491Z", + "iopub.status.idle": "2024-06-19T15:53:52.017565Z", + "shell.execute_reply": "2024-06-19T15:53:52.017225Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -244,16 +254,16 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:31.073694Z", - "iopub.status.busy": "2023-07-20T21:47:31.073256Z", - "iopub.status.idle": "2023-07-20T21:47:31.841399Z", - "shell.execute_reply": "2023-07-20T21:47:31.840742Z" + "iopub.execute_input": "2024-06-19T15:53:52.019417Z", + "iopub.status.busy": "2024-06-19T15:53:52.019210Z", + "iopub.status.idle": "2024-06-19T15:53:52.285053Z", + "shell.execute_reply": "2024-06-19T15:53:52.284675Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -288,10 +298,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:31.844430Z", - "iopub.status.busy": "2023-07-20T21:47:31.844021Z", - "iopub.status.idle": "2023-07-20T21:47:31.848830Z", - "shell.execute_reply": "2023-07-20T21:47:31.848203Z" + "iopub.execute_input": "2024-06-19T15:53:52.286698Z", + "iopub.status.busy": "2024-06-19T15:53:52.286557Z", + "iopub.status.idle": "2024-06-19T15:53:52.289426Z", + "shell.execute_reply": "2024-06-19T15:53:52.289131Z" } }, "outputs": [ @@ -355,10 +365,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:31.851732Z", - "iopub.status.busy": "2023-07-20T21:47:31.851250Z", - "iopub.status.idle": "2023-07-20T21:47:54.060544Z", - "shell.execute_reply": "2023-07-20T21:47:54.047413Z" + "iopub.execute_input": "2024-06-19T15:53:52.290768Z", + "iopub.status.busy": "2024-06-19T15:53:52.290662Z", + "iopub.status.idle": "2024-06-19T15:53:52.887343Z", + "shell.execute_reply": "2024-06-19T15:53:52.887015Z" } }, "outputs": [ @@ -366,122 +376,91 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-07-20 21:47:39.219410: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-07-20 21:47:39.784075: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10810 MB memory: -> device: 0, name: Tesla K80, pci bus id: 0001:00:00.0, compute capability: 3.7\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-07-20 21:47:46.945498: I tensorflow/core/util/cuda_solvers.cc:179] Creating GpuSolver handles for stream 0x55fd454ceef0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "_________________________________________________________________\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Layer (type) Output Shape Param # \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " input_1 (InputLayer) [(None, 784, 1)] 0 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " lmu (LMU) (None, 212) 99641 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " dense (Dense) (None, 10) 2130 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n" + "2024-06-19 15:53:52.304656: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-06-19 15:53:52.305088: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", + "Skipping registering GPU devices...\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "=================================================================\n" - ] + "data": { + "text/html": [ + "
Model: \"functional_1\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer (InputLayer)        │ (None, 784, 1)         │             0 │\n",
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+       "│ lmu (LMU)                       │ (None, 212)            │        99,641 │\n",
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+       "│ dense (Dense)                   │ (None, 10)             │         2,130 │\n",
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+       "
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", + " loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " optimizer=\"adam\",\n", " metrics=[\"accuracy\"],\n", ")\n", @@ -541,10 +520,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:54.064136Z", - "iopub.status.busy": "2023-07-20T21:47:54.063794Z", - "iopub.status.idle": "2023-07-20T21:47:54.069240Z", - "shell.execute_reply": "2023-07-20T21:47:54.067998Z" + "iopub.execute_input": "2024-06-19T15:53:52.888756Z", + "iopub.status.busy": "2024-06-19T15:53:52.888615Z", + "iopub.status.idle": "2024-06-19T15:53:52.891189Z", + "shell.execute_reply": "2024-06-19T15:53:52.890897Z" } }, "outputs": [], @@ -553,10 +532,13 @@ "batch_size = 100\n", "epochs = 10\n", "\n", - "saved_weights_fname = \"./psMNIST-weights.hdf5\"\n", + "saved_model_fname = \"./psMNIST.keras\"\n", "callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=saved_weights_fname, monitor=\"val_loss\", verbose=1, save_best_only=True\n", + " keras.callbacks.ModelCheckpoint(\n", + " filepath=saved_model_fname,\n", + " monitor=\"val_accuracy\",\n", + " verbose=1,\n", + " save_best_only=True,\n", " ),\n", "]\n", "\n", @@ -585,16 +567,16 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:54.083744Z", - "iopub.status.busy": "2023-07-20T21:47:54.071979Z", - "iopub.status.idle": "2023-07-20T21:47:55.192322Z", - "shell.execute_reply": "2023-07-20T21:47:55.191645Z" + "iopub.execute_input": "2024-06-19T15:53:52.892817Z", + "iopub.status.busy": "2024-06-19T15:53:52.892636Z", + "iopub.status.idle": "2024-06-19T15:53:52.896182Z", + "shell.execute_reply": "2024-06-19T15:53:52.895904Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "" ] @@ -647,2536 +629,32 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-07-20T21:47:55.197344Z", - "iopub.status.busy": "2023-07-20T21:47:55.196879Z", - "iopub.status.idle": "2023-07-20T21:48:19.837809Z", - "shell.execute_reply": "2023-07-20T21:48:19.837135Z" + "iopub.execute_input": "2024-06-19T15:53:52.897754Z", + "iopub.status.busy": "2024-06-19T15:53:52.897651Z", + "iopub.status.idle": "2024-06-19T15:54:19.330575Z", + "shell.execute_reply": "2024-06-19T15:54:19.329876Z" } }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/313 [..............................] - ETA: 3:57 - loss: 0.0459 - accuracy: 0.9688" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 2/313 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"name": "stderr", "output_type": "stream", "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "313/313 [==============================] - 25s 76ms/step - loss: 0.1210 - accuracy: 0.9643\n" + "/home/runner/micromamba/envs/ci/lib/python3.9/site-packages/keras/src/saving/saving_lib.py:415: UserWarning: Skipping variable loading for optimizer 'adam', because it has 2 variables whereas the saved optimizer has 14 variables. \n", + " saveable.load_own_variables(weights_store.get(inner_path))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Test accuracy: 96.43%\n" + "Test accuracy: 96.41%\n" ] } ], "source": [ - "model.load_weights(saved_weights_fname)\n", - "accuracy = model.evaluate(X_test, Y_test)[1] * 100\n", + "model.load_weights(saved_model_fname)\n", + "accuracy = model.evaluate(X_test, Y_test, verbose=0)[1] * 100\n", "print(f\"Test accuracy: {round(accuracy, 2):0.2f}%\")" ] }, @@ -3201,7 +679,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.19" } }, "nbformat": 4, diff --git a/.doctrees/nbsphinx/examples_psMNIST_11_0.png b/.doctrees/nbsphinx/examples_psMNIST_11_0.png index 5579e4fa..0504bbb8 100644 Binary files a/.doctrees/nbsphinx/examples_psMNIST_11_0.png and b/.doctrees/nbsphinx/examples_psMNIST_11_0.png differ diff --git a/.doctrees/nbsphinx/examples_psMNIST_13_0.png b/.doctrees/nbsphinx/examples_psMNIST_13_0.png index 6cadd3ae..3e4850ab 100644 Binary files a/.doctrees/nbsphinx/examples_psMNIST_13_0.png and b/.doctrees/nbsphinx/examples_psMNIST_13_0.png differ diff --git a/.doctrees/nbsphinx/examples_psMNIST_23_0.png b/.doctrees/nbsphinx/examples_psMNIST_23_0.png index dc720994..946a86dd 100644 Binary files a/.doctrees/nbsphinx/examples_psMNIST_23_0.png and b/.doctrees/nbsphinx/examples_psMNIST_23_0.png differ diff --git a/.doctrees/nbsphinx/examples_psMNIST_9_0.png b/.doctrees/nbsphinx/examples_psMNIST_9_0.png index 7a47aa30..cd8fe494 100644 Binary files a/.doctrees/nbsphinx/examples_psMNIST_9_0.png and b/.doctrees/nbsphinx/examples_psMNIST_9_0.png differ diff --git a/.doctrees/project.doctree b/.doctrees/project.doctree index a2036bf7..29386800 100644 Binary files a/.doctrees/project.doctree and b/.doctrees/project.doctree differ diff --git a/404.html b/404.html index f507bcfe..5431b344 100644 --- a/404.html +++ b/404.html @@ -1,19 +1,17 @@ - - + - Page not found — KerasLMU 0.7.0 docs - - - + Page not found — KerasLMU 0.8.0.dev0 docs + + + -