=
127 lines
2.5 KiB
Plaintext
127 lines
2.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Parameters"
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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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"batch_size = 8\n",
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"learning_rate = 0.0001\n",
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"epochs = 10"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Define model"
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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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"from model import DepthEstimate\n",
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"\n",
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"model = DepthEstimate()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Data loader"
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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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"from data import DataLoader\n",
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"\n",
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"dl = DataLoader()\n",
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"train_generator = dl.get_batched_dataset(batch_size)\n",
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"\n",
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"print('Data loader ready.')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Compile & Train"
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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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"import tensorflow\n",
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"from loss import depth_loss_function\n",
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"\n",
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"optimizer = tensorflow.keras.optimizers.Adam(lr=learning_rate, amsgrad=True)\n",
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"\n",
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"model.compile(loss=depth_loss_function, optimizer=optimizer)"
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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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"# Create checkpoint callback\n",
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"import os\n",
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"checkpoint_path = \"training_1/cp.ckpt\"\n",
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"checkpoint_dir = os.path.dirname(checkpoint_path)\n",
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"cp_callback = tensorflow.keras.callbacks.ModelCheckpoint(checkpoint_path, save_weights_only=True, verbose=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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"# Start training\n",
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"model.fit(train_generator, epochs=5, steps_per_epoch=dl.length//batch_size, callbacks=[cp_callback])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [conda env:tf_gpu] *",
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"language": "python",
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"name": "conda-env-tf_gpu-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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