tensorboard train cluster
parent
b085401e6e
commit
cb693d6cc1
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@ -26,7 +26,7 @@
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"import os\n",
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"import numpy as np\n",
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"import simplejson as json\n",
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"from datetime import datetime"
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"import datetime"
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]
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},
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{
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@ -89,8 +89,8 @@
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"source": [
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"IMG_HEIGHT = 416\n",
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"IMG_WIDTH= 804\n",
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"batch_size = 1\n",
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"epochs = 1\n",
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"batch_size = 4\n",
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"epochs = 4\n",
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"# Full size, machine barfs probably needs more RAM\n",
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"#IMG_HEIGHT = 832\n",
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"#IMG_WIDTH = 1606\n",
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@ -163,8 +163,8 @@
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"source": [
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"print(\"--\")\n",
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"print(\"Reduce training and validation set when testing\")\n",
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"total_train = 1\n",
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"total_val = 1\n",
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"total_train = 16\n",
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"total_val = 16\n",
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"print(\"Reduced training images:\", total_train)\n",
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"print(\"Reduced validation images:\", total_val)"
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]
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@ -221,9 +221,18 @@
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"outputs": [],
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"source": [
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"%load_ext tensorboard\n",
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"#os.mkdir(\"cluster-logs\")\n",
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"logdir = \"cluster-logs\"\n",
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"tensorboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir=logdir)"
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"!rm -rf ./cluster-logs/\n",
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"log_dir=\"cluster-logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
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"tensorboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%tensorboard --logdir cluster-logs/fit"
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]
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},
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{
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@ -286,15 +295,6 @@
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"Image.LOAD_TRUNCATED_IMAGES = True"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%tensorboard --logdir cluster-logs/"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -303,19 +303,15 @@
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"source": [
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"acc = history.history['accuracy']\n",
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"val_acc = history.history['val_accuracy']\n",
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"\n",
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"loss = history.history['loss']\n",
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"val_loss = history.history['val_loss']\n",
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"\n",
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"epochs_range = range(epochs)\n",
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"\n",
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"plt.figure(figsize=(8, 8))\n",
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"plt.subplot(1, 2, 1)\n",
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"plt.plot(epochs_range, acc, label='Training Accuracy')\n",
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"plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
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"plt.legend(loc='lower right')\n",
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"plt.title('Training and Validation Accuracy')\n",
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"\n",
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"plt.subplot(1, 2, 2)\n",
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"plt.plot(epochs_range, loss, label='Training Loss')\n",
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"plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
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