From 42592fed4aad3f9c01f5f15a80a8ec82859c19ab Mon Sep 17 00:00:00 2001 From: Michelle Casbon Date: Tue, 11 Sep 2018 14:17:02 -0400 Subject: [PATCH] Update demo script & add notebook (#248) * Update demo script Update demo script to include deploy script and notebook created by @drscott173 Simplify by removing unnecessary commands Use default namespace instead of kubeflow * Add yelp notebook readme * Add cluster creation commands Add instructions for highlighting changes resulting from each command --- demos/yelp_demo/README.md | 238 ++-- demos/yelp_demo/demo_setup/README.md | 14 - .../demo_setup/kubeflow-demo-base.env | 4 +- demos/yelp_demo/notebooks/README.md | 39 + demos/yelp_demo/notebooks/noise.png | Bin 0 -> 37325 bytes demos/yelp_demo/notebooks/semantics.png | Bin 0 -> 113153 bytes demos/yelp_demo/notebooks/signal.png | Bin 0 -> 37388 bytes demos/yelp_demo/notebooks/yelp.ipynb | 1248 +++++++++++++++++ 8 files changed, 1419 insertions(+), 124 deletions(-) create mode 100644 demos/yelp_demo/notebooks/README.md create mode 100644 demos/yelp_demo/notebooks/noise.png create mode 100644 demos/yelp_demo/notebooks/semantics.png create mode 100644 demos/yelp_demo/notebooks/signal.png create mode 100644 demos/yelp_demo/notebooks/yelp.ipynb diff --git a/demos/yelp_demo/README.md b/demos/yelp_demo/README.md index 42e0cf01..73e97363 100644 --- a/demos/yelp_demo/README.md +++ b/demos/yelp_demo/README.md @@ -6,75 +6,77 @@ presentation to public audiences. The base demo includes the following steps: 1. [Install kubeflow locally](#1-install-kubeflow-locally) -1. [Bring up a notebook](#2-bring-up-a-notebook) -1. [Run training locally](#3-run-training-locally) -1. [Install kubeflow on GKE](#4-install-kubeflow-on-gke) -1. [Run training on GKE](#5-run-training-on-gke) -1. [Create the serving and UI components](#6-create-the-serving-and-ui-components) +1. [Run training locally](#2-run-training-locally) +1. [Install kubeflow on GKE](#3-install-kubeflow-on-gke) +1. [Run training on GKE](#4-run-training-on-gke) +1. [Create the serving and UI components](#5-create-the-serving-and-ui-components) +1. [Bring up a notebook](#6-bring-up-a-notebook) ## Important! Pre-work -Before completing any of the below steps, follow the instructions in [./demo_setup](./demo_setup/README.md) to prepare for demonstrating Kubeflow. +Before completing any of the below steps, follow the instructions in +[./demo_setup](https://github.com/kubeflow/examples/blob/master/demos/yelp_demo/demo_setup/README.md) +to prepare for demonstrating Kubeflow: + +* [Create a minikube cluster](https://github.com/kubeflow/examples/blob/master/demos/yelp_demo/demo_setup/README.md#4-create-a-minikube-cluster) +* [Create a GKE cluster](https://github.com/kubeflow/examples/blob/master/demos/yelp_demo/demo_setup/README.md#5-create-a-gke-cluster) + +## 1. Create clusters + +[Setup your environment](https://github.com/kubeflow/examples/tree/master/demos/yelp_demo/demo_setup#2-set-environment-variables) +or source the base file: + +``` +cd demo_setup +source kubeflow-demo-base.env +``` + +Create a minikube cluster: + +``` +minikube start \ + --cpus 4 \ + --memory 8096 \ + --disk-size=50g \ + --kubernetes-version v1.10.6 +``` + +Create a GKE cluster with access to GPUs and TPUs: + +``` +gcloud beta container clusters create ${CLUSTER} \ + --project ${DEMO_PROJECT} \ + --zone ${ZONE} \ + --accelerator type=nvidia-tesla-k80,count=2 \ + --cluster-version 1.10.6-gke.2 \ + --enable-ip-alias \ + --enable-tpu \ + --machine-type n1-highmem-8 \ + --scopes cloud-platform,compute-rw,storage-rw \ + --verbosity error +``` ## 1. Install kubeflow locally -Initialize a ksonnet app: +Run the following script to create a ksonnet app for Kubeflow and deploy it: ``` -ks init kubeflow -cd kubeflow +export KUBEFLOW_VERSION=0.2.5 +curl https://raw.githubusercontent.com/kubeflow/kubeflow/v${KUBEFLOW_VERSION}/scripts/deploy.sh | bash ``` -Install packages and generate core components: +View the installed components: ``` -ks registry add kubeflow github.com/kubeflow/kubeflow/tree/${VERSION}/kubeflow -ks pkg install kubeflow/core@${VERSION} -ks pkg install kubeflow/tf-serving@${VERSION} -ks pkg install kubeflow/tf-job@${VERSION} -ks generate core kubeflow-core --name=kubeflow-core +kubectl get pod ``` -Create the minikube environment and set the cloud parameter: - -``` -ks env add minikube --namespace=${NAMESPACE} -ks param set --env minikube kubeflow-core \ - cloud "minikube" -``` - -Apply kubeflow to the cluster: - -``` -ks apply minikube -c kubeflow-core -``` - -## 2. Bring up a notebook - -Connect to Jupyterhub by forwarding a port and opening a browser to -[localhost:8000](localhost:8000): - -``` -kubectl port-forward tf-hub-0 8000:8000 -``` - -Spawn a new pod with this image: - -``` -gcr.io/kubeflow-dev/issue-summarization-notebook-cpu:latest -``` - -Once the notebook environment is -available, open a new terminal and upload this [Simple ML Model -notebook](notebooks/simple_ml_model.ipynb). - -Execute the notebook to show that it works. - -## 3. Run training locally +## 2. Run training locally Retrieve the following files for the t2tcpu & t2ttpu jobs: ``` +cd kubeflow_ks_app cp ${REPO_PATH}/demo/components/t2t[ct]pu.* components cp ${REPO_PATH}/demo/components/params.* components ``` @@ -82,43 +84,42 @@ cp ${REPO_PATH}/demo/components/params.* components Set parameter values for training: ``` -ks param set --env minikube t2tcpu \ +ks param set --env default t2tcpu \ cloud "minikube" -ks param set --env minikube t2tcpu \ - dataDir ${GCS_TRAINING_DATA_DIR} -ks param set --env minikube t2tcpu \ - outputGCSPath ${GCS_TRAINING_OUTPUT_DIR_LOCAL} -ks param set --env minikube t2tcpu \ - cpuImage gcr.io/${DEMO_PROJECT}/kubeflow-yelp-demo-cpu:latest -ks param set --env minikube t2tcpu \ +ks param set --env default t2tcpu \ workers 2 ``` Generate manifests and apply to cluster: ``` -ks apply minikube -c t2tcpu +ks apply default -c t2tcpu ``` -## 4. Install kubeflow on GKE +View the new training pod and wait until it has a `Running` status: + +``` +kubectl get pod +``` + +View the logs to watch training commence: + +``` +kubectl logs -f t2tcpu-master-0 | grep INFO:tensorflow +``` + +## 3. Install kubeflow on GKE Switch to a GKE cluster: ``` -kubectl config use-context gke +kubectl config use gke ``` Create an environment: ``` -ks env add gke --namespace=${NAMESPACE} -``` - -Set parameter values for kubeflow-core: - -``` -ks param set --env gke kubeflow-core \ - cloud "gke" +ks env add gke ``` Install kubeflow on the cluster: @@ -127,15 +128,19 @@ Install kubeflow on the cluster: ks apply gke -c kubeflow-core ``` -## 5. Run training on GKE +View the installed components: + +``` +kubectl get pod +``` + +## 4. Run training on GKE ### Distributed CPU training Set parameter values for training: ``` -ks param set --env gke t2tcpu \ - dataDir ${GCS_TRAINING_DATA_DIR} ks param set --env gke t2tcpu \ outputGCSPath ${GCS_TRAINING_OUTPUT_DIR_CPU} ``` @@ -147,19 +152,16 @@ above 1000. ks apply gke -c t2tcpu ``` -#### Export the trained model - -This will export the model to an `export/` directory in output_dir. +View the new training pod and wait until it has a `Running` status: ``` -cd ../yelp -t2t-exporter \ - --t2t_usr_dir=${USR_DIR} \ - --model=${MODEL} \ - --hparams_set=${HPARAMS_SET} \ - --problem=${PROBLEM} \ - --data_dir=${GCS_TRAINING_DATA_DIR} \ - --output_dir=${GCS_TRAINING_OUTPUT_DIR_CPU} +kubectl get pod +``` + +View the logs to watch training commence: + +``` +kubectl logs -f t2tcpu-master-0 | grep INFO:tensorflow ``` ### Distributed TPU training @@ -167,8 +169,6 @@ t2t-exporter \ Set parameter values for training: ``` -ks param set --env gke t2ttpu \ - dataDir ${GCS_TRAINING_DATA_DIR} ks param set --env gke t2ttpu \ outputGCSPath ${GCS_TRAINING_OUTPUT_DIR_TPU} ``` @@ -180,22 +180,21 @@ above 1000. ks apply gke -c t2ttpu ``` -#### Export the trained model - -This will export the model to an `export/` directory in output_dir. +Verify that a TPU is being provisioned by viewing pod status. It should remain +in Pending state for 3-4 minutes with the message +`Creating Cloud TPUs for pod default/t2ttpu-master-0`. ``` -cd ../yelp -t2t-exporter \ - --t2t_usr_dir=${USR_DIR} \ - --model=${MODEL} \ - --hparams_set=${HPARAMS_SET} \ - --problem=${PROBLEM} \ - --data_dir=${GCS_TRAINING_DATA_DIR} \ - --output_dir=${GCS_TRAINING_OUTPUT_DIR_TPU} +kubectl describe pod t2ttpu-master-0 ``` -## 6. Create the serving and UI components +Once it has `Running` status, view the logs to watch training commence: + +``` +kubectl logs -f t2ttpu-master-0 | grep INFO:tensorflow +``` + +## 5. Create the serving and UI components Retrieve the following files for the serving & UI components: @@ -204,14 +203,6 @@ cp ${REPO_PATH}/demo/components/serving.* components cp ${REPO_PATH}/demo/components/ui.* components ``` - -Set parameter values for serving: - -``` -ks param set --env gke serving \ - modelPath ${GCS_TRAINING_OUTPUT_DIR_TPU}/export/Servo -``` - Create the serving and UI components: ``` @@ -228,8 +219,8 @@ UI_POD=$(kubectl get po -l app=kubeflow-demo-ui | \ kubectl port-forward ${UI_POD} 8080:80 ``` -Optional: Setup an SSH tunnel from your local laptop into the GCE instance connecting to -GKE: +Optional: If necessary, setup an SSH tunnel from your local laptop into the +compute instance connecting to GKE: ``` ssh $HOST -L 8080:localhost:8080 @@ -240,4 +231,35 @@ To show the naive version, navigate to [localhost:8080](localhost:8080) from a b To show the ML version, navigate to [localhost:8080/kubeflow](localhost:8080/kubeflow) from a browser. +## 6. Bring up a notebook + +Connect to the Central Dashboard by forwarding a port to one of the ambassador +pods: + +``` +AMBASSADOR_POD=$(kubectl get po -l service=ambassador | \ + grep ambassador | \ + head -n 1 | \ + cut -d " " -f 1 \ +) +kubectl port-forward ${AMBASSADOR_POD} 8081:80 +``` + +Open a browser and connect to [localhost:8081](localhost:8081). +Show the TF-job dashboard, then click on Jupyterhub. +Log in with any username and password combination and wait until the page +refreshes. Spawn a new pod with these resource requirements: + +| Resource | Value | +| --------------------- | -------------------------------------------------------------------- | +| Image | `gcr.io/kubeflow-images-public/tensorflow-1.7.0-notebook-gpu:v0.2.1` | +| CPU | 2 | +| Memory | 48G | +| Extra Resource Limits | `{"nvidia.com/gpu":2}` | + +Once the notebook environment is +available, open a new terminal and upload this +[Yelp notebook](notebooks/yelp.ipynb). + +Execute the notebook to show that it works. diff --git a/demos/yelp_demo/demo_setup/README.md b/demos/yelp_demo/demo_setup/README.md index c47fff5a..a467c10e 100644 --- a/demos/yelp_demo/demo_setup/README.md +++ b/demos/yelp_demo/demo_setup/README.md @@ -340,20 +340,6 @@ minikube start \ --kubernetes-version v1.10.6 ``` -RBAC permissions allow your user to install kubeflow components on the cluster. - -``` -kubectl create clusterrolebinding cluster-admin-binding-${USER} \ - --clusterrole cluster-admin \ - --user $(gcloud config get-value account) -./create_context.sh minikube ${NAMESPACE} -``` - -Create a namespace: -``` -kubectl create namespace ${NAMESPACE} -``` - ### Create k8s secrets Since our project is private, we need to provide access to resources via the use diff --git a/demos/yelp_demo/demo_setup/kubeflow-demo-base.env b/demos/yelp_demo/demo_setup/kubeflow-demo-base.env index 05579299..e211f780 100644 --- a/demos/yelp_demo/demo_setup/kubeflow-demo-base.env +++ b/demos/yelp_demo/demo_setup/kubeflow-demo-base.env @@ -1,12 +1,12 @@ export DEMO_PROJECT=kubeflow-demo-base -export NAMESPACE=kubeflow +export NAMESPACE=default export ZONE=us-central1-a export CLUSTER=demo # Makefile uses project. export PROJECT=${DEMO_PROJECT} export ENV=gke export SVC_ACCT=minikube -export VERSION=v0.2.5 +export KUBEFLOW_VERSION=v0.2.5 export REPO_PATH=${HOME}/repos/kubeflow/examples/demos/yelp_demo export MAX_CASES=1000000 diff --git a/demos/yelp_demo/notebooks/README.md b/demos/yelp_demo/notebooks/README.md new file mode 100644 index 00000000..560b44de --- /dev/null +++ b/demos/yelp_demo/notebooks/README.md @@ -0,0 +1,39 @@ +## Yelp Sentiment Notebook Demo +### yelp.ipynb + +We're trying to create a neural network that detects sentiment, and +predicts whether a Yelp review (all 5 million of them) is positive or negative. +Reviews come from words. Maybe we can look at the set of words in a review and make a guess from that? + +Well let's see. How frequently do some words occur? Let's grab a sample of 100k positive and +negative reviews, 200k total. + +![Lots of noise](noise.png) + +The chart on the left shows that most words from 200,000 +reviews are indistinguishable en masse, +so that's why naive approaches may take a long time to converge. +One approach is to look at words that occur more frequently. +When we cut off words that occur less than 50 times, we get +the chart on the right. We see the beginning of a distro, but it's weak. +We have to clean up the signal for our network +using a hypothesis of what's important. + +![Signal](signal.png) + +So we calculate a ratio, how many words appear +in positive vs. negative reviews? A value of 0 means +all negative reviews, 1.0 means equal distribution between +positive and negative, greater than 1.0 means more positive than +negative. Neural networks like values between -1 and 1 centered at 0. +Take the log(x)! log(1) = 0, log(1/x) = negative, log(n) = small. +Then lets cut out those that don't help much, the area around 0. +Voila. A signal around 0, on the right! Now let's train that on a simple network. + +![Results](semantics.png) + +The network converges in a few minutes with 94% accuracy. +The weights on the input nodes are a vector representation of +semantics, so vectors close together are similar. +Here we see words that are somewhat similar, with some noise, +as we project from a 64-dimensional space to 2 dimensions. 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if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"

\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(\"9cd40ca9-0299-434f-b01d-4975b1483b7a\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };var element = document.getElementById(\"9cd40ca9-0299-434f-b01d-4975b1483b7a\");\n", + " if (element == null) {\n", + " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '9cd40ca9-0299-434f-b01d-4975b1483b7a' but no matching script tag was found. \")\n", + " return false;\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(\"9cd40ca9-0299-434f-b01d-4975b1483b7a\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"9cd40ca9-0299-434f-b01d-4975b1483b7a\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"9cd40ca9-0299-434f-b01d-4975b1483b7a\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '9cd40ca9-0299-434f-b01d-4975b1483b7a' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"9cd40ca9-0299-434f-b01d-4975b1483b7a\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bokeh.models import ColumnDataSource, LabelSet\n", + "from bokeh.plotting import figure, show, output_file\n", + "from bokeh.io import output_notebook\n", + "output_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5261669/5261669 [00:45<00:00, 116165.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 5261669 reviews in 0:01:22.839652\n", + "Love the staff, love the meat, love the place. Prepare for a long line around lunch or dinner hours. \n", + "\n", + "They ask you how you want you meat, lean or something maybe, I can't remember. Just say you don't want it too fatty. \n", + "\n", + "Get a half sour pickle and a hot pepper. Hand cut french fries too. \n", + "\n", + "Rating: 5 stars\n" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "from collections import Counter\n", + "from datetime import datetime\n", + "import json\n", + "\n", + "t1 = datetime.now()\n", + "print(\"Loading...\")\n", + "with open(\"yelp_reviews.json\", \"r\", encoding=\"utf-8\") as f:\n", + " reviews = f.read().strip().split(\"\\n\")\n", + "reviews = [json.loads(review) for review in tqdm(reviews)]\n", + "print(\"Loaded \", len(reviews), \"reviews in \", datetime.now() - t1)\n", + "\n", + "print(reviews[0]['text'], \"\\n\\nRating: \", reviews[0]['stars'],\"stars\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 9%|▊ | 449324/5261669 [00:06<01:09, 69264.96it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected 100000 positive and 100000 negative reviews\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# choose a random subset of reviews\n", + "count = 100000\n", + "import numpy as np\n", + "import re\n", + "\n", + "np.random.seed(1)\n", + "positive = []\n", + "negative = []\n", + "all_reviews = np.array(reviews)\n", + "np.random.shuffle(all_reviews)\n", + "notalpha = re.compile('[^a-zA-Z ]')\n", + "\n", + "def tokenize(text):\n", + " return notalpha.sub('',text).lower().strip()\n", + " \n", + "for review in tqdm(all_reviews):\n", + " neg = review['stars'] < 3\n", + " pos = review['stars'] > 3\n", + " text = tokenize(review['text'])\n", + " if neg and len(negative) < count:\n", + " negative.append(text)\n", + " elif pos and len(positive) < count:\n", + " positive.append(text)\n", + " if len(negative) >= count and len(positive) >= count:\n", + " break\n", + "\n", + "print(\"Selected \",len(positive),\"positive and\",len(negative),\"negative reviews\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please stand by...\n", + "Memory cleared\n" + ] + } + ], + "source": [ + "# clean up memory\n", + "print(\"Please stand by...\")\n", + "reviews = []\n", + "all_reviews = []\n", + "print(\"Memory cleared\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 1%| | 964/100000 [00:00<00:10, 9635.25it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gathering positive words\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100000/100000 [00:10<00:00, 9626.41it/s]\n", + " 1%| | 672/100000 [00:00<00:14, 6718.00it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gathering negative words\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100000/100000 [00:15<00:00, 6568.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating influence ratios of frequent words\n", + "Creating training vocabulary\n", + "Found 9541 words\n" + ] + } + ], + "source": [ + "from collections import Counter\n", + "\n", + "positive_counts = Counter()\n", + "negative_counts = Counter()\n", + "total_counts = Counter()\n", + "pos_neg_ratios = Counter()\n", + "polarity_cutoff = 0.2\n", + "min_count = 50\n", + "\n", + "print(\"Gathering positive words\")\n", + "for review in tqdm(positive):\n", + " for word in review.split(\" \"):\n", + " positive_counts[word] += 1\n", + " total_counts[word] += 1\n", + "\n", + "print(\"Gathering negative words\")\n", + "for review in tqdm(negative):\n", + " for word in review.split(\" \"):\n", + " negative_counts[word] += 1\n", + " total_counts[word] += 1\n", + " \n", + "print(\"Creating influence ratios of frequent words\")\n", + "for term,cnt in list(total_counts.most_common()):\n", + " if(cnt >= min_count):\n", + " pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n", + " pos_neg_ratios[term] = pos_neg_ratio\n", + "\n", + "for word,ratio in pos_neg_ratios.most_common():\n", + " if(ratio > 1):\n", + " pos_neg_ratios[word] = np.log(ratio)\n", + " else:\n", + " pos_neg_ratios[word] = -np.log((1 / (ratio + 0.01)))\n", + "\n", + "print(\"Creating training vocabulary\")\n", + "review_vocab = set()\n", + "for word,p in pos_neg_ratios.most_common():\n", + " if (p >= polarity_cutoff or p <= -polarity_cutoff):\n", + " review_vocab.add(word)\n", + " \n", + "print(\"Found\",len(review_vocab),\"words\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Application\",\"version\":\"0.13.0\"}};\n", + " var render_items = [{\"docid\":\"c5082536-8d38-48cc-b93b-0f8c229390dd\",\"roots\":{\"dec1db7c-ba41-4023-84c4-23da64b012ce\":\"515dda20-28ec-47e6-93c3-ad781336c903\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " clearInterval(timer);\n", + " }\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", + " clearInterval(timer);\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "dec1db7c-ba41-4023-84c4-23da64b012ce" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from bokeh.layouts import gridplot\n", + "from bokeh.plotting import figure, show, output_file\n", + "\n", + "p1 = figure(title=\"Discriminating Word Distribution\",tools=\"save\",\n", + " background_fill_color=\"#E8DDCB\")\n", + "\n", + "measured = []\n", + "for word in review_vocab:\n", + " measured.append(pos_neg_ratios[word])\n", + " \n", + "measured = np.array(measured)\n", + "hist, edges = np.histogram(measured, density=True, bins=50)\n", + "\n", + "p1.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n", + " fill_color=\"#036564\", line_color=\"#033649\")\n", + "\n", + "p1.legend.location = \"center_right\"\n", + "p1.legend.background_fill_color = \"darkgrey\"\n", + "p1.xaxis.axis_label = 'log(pos/neg ratio)'\n", + "p1.yaxis.axis_label = 'relative count'\n", + "\n", + "p2 = figure(title=\"Raw Word Distribution\",tools=\"save\",\n", + " background_fill_color=\"#E8DDCB\")\n", + "\n", + "measured = []\n", + "for word in total_counts:\n", + " measured.append(total_counts[word])\n", + " \n", + "measured = np.array(measured)\n", + "hist, edges = np.histogram(measured, density=True, bins=50)\n", + "\n", + "p2.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n", + " fill_color=\"#036564\", line_color=\"#033649\")\n", + "\n", + "p2.legend.location = \"center_right\"\n", + "p2.legend.background_fill_color = \"darkgrey\"\n", + "p2.xaxis.axis_label = 'word frequency'\n", + "p2.yaxis.axis_label = 'relative count'\n", + "\n", + "p3 = figure(title=\"Min Count Word Distribution\",tools=\"save\",\n", + " background_fill_color=\"#E8DDCB\")\n", + "\n", + "measured = []\n", + "for word in total_counts:\n", + " c = total_counts[word]\n", + " if c > min_count:\n", + " measured.append(c)\n", + " \n", + "measured = np.array(measured)\n", + "hist, edges = np.histogram(measured, density=True, bins=50)\n", + "\n", + "p3.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n", + " fill_color=\"#036564\", line_color=\"#033649\")\n", + "\n", + "p3.legend.location = \"center_right\"\n", + "p3.legend.background_fill_color = \"darkgrey\"\n", + "p3.xaxis.axis_label = 'word frequency (above cutoff)'\n", + "p3.yaxis.axis_label = 'relative count'\n", + "\n", + "p4 = figure(title=\"Pos/Neg Distribution\",tools=\"save\",\n", + " background_fill_color=\"#E8DDCB\")\n", + "\n", + "measured = []\n", + "for term,cnt in list(total_counts.most_common()):\n", + " if(cnt >= min_count):\n", + " pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n", + " measured.append(pos_neg_ratio)\n", + " \n", + "measured = np.array(measured)\n", + "hist, edges = np.histogram(measured, density=True, bins=50)\n", + "\n", + "p4.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n", + " fill_color=\"#036564\", line_color=\"#033649\")\n", + "\n", + "p4.legend.location = \"center_right\"\n", + "p4.legend.background_fill_color = \"darkgrey\"\n", + "p4.xaxis.axis_label = 'pos/neg ratio'\n", + "p4.yaxis.axis_label = 'relative count'\n", + "\n", + "show(gridplot(p2,p3,p4,p1, ncols=2, plot_width=400, plot_height=400, toolbar_location=None))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|▏ | 2328/100000 [00:00<00:08, 11632.00it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating 1-hot positive encodings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100000/100000 [00:08<00:00, 11171.87it/s]\n", + " 1%| | 886/100000 [00:00<00:11, 8838.60it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating 1-hot negative encodings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100000/100000 [00:12<00:00, 8294.29it/s]\n" + ] + } + ], + "source": [ + "# create mappings from words to numbers and vice versa\n", + "\n", + "word2index = {}\n", + "index2word = {}\n", + "for i, word in enumerate(review_vocab):\n", + " word2index[word] = i\n", + " index2word[i] = word\n", + "\n", + "n = len(review_vocab)\n", + "samples = len(positive)+len(negative)\n", + "all_words = word2index.keys()\n", + "\n", + "# encode 1-hot reviews\n", + "x = np.zeros((samples, n))\n", + "y = np.zeros((samples,2))\n", + "\n", + "idx = -1\n", + "print(\"Creating 1-hot positive encodings\")\n", + "for review in tqdm(positive):\n", + " idx += 1\n", + " for word in review.split(\" \"):\n", + " if word in all_words:\n", + " x[idx, word2index[word]] = 1\n", + " y[idx,0] = 1\n", + " \n", + "print(\"Creating 1-hot negative encodings\")\n", + "for review in tqdm(negative):\n", + " idx += 1\n", + " for word in review.split(\" \"):\n", + " if word in all_words:\n", + " x[idx, word2index[word]] = 1\n", + " y[idx,1] = 1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "# We'll bundle groups of examples during training for efficiency.\n", + "# This defines the size of the batch.\n", + "BATCH_SIZE = 100\n", + "VOCAB_SIZE = len(review_vocab)\n", + "EMBEDDING_SIZE = 64\n", + "NUM_LABELS = 2\n", + "NUM_GPUS = 2\n", + "LEARNING_RATE = 0.0005\n", + "DISPLAY_STEP = 100\n", + "NUM_STEPS = 2000\n", + "\n", + "# The random seed that defines initialization.\n", + "SEED = 42\n", + "\n", + "def model(x, prefix='model', reuse=True, is_training=True):\n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('Model', reuse=reuse):\n", + " nn = tf.layers.dense(x, EMBEDDING_SIZE, activation=tf.nn.sigmoid, name=prefix+'_embedding')\n", + " nn = tf.layers.dense(nn, NUM_LABELS, activation=tf.nn.sigmoid, name=prefix+'_logits')\n", + " # We only apply need to apply softmax to testing network\n", + " out = tf.nn.softmax(nn) if not is_training else nn\n", + " return out\n", + "\n", + "print('Done')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done\n" + ] + } + ], + "source": [ + "# Build the function to average the gradients\n", + "def average_gradients(tower_grads):\n", + " average_grads = []\n", + " \n", + " # print(tower_grads)\n", + "\n", + " for grad_and_vars in zip(*tower_grads):\n", + " # Note that each grad_and_vars looks like the following:\n", + " # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))\n", + " grads = []\n", + " for g, _ in grad_and_vars:\n", + " # Add 0 dimension to the gradients to represent the tower.\n", + " expanded_g = tf.expand_dims(g, 0)\n", + "\n", + " # Append on a 'tower' dimension which we will average over below.\n", + " grads.append(expanded_g)\n", + "\n", + " # Average over the 'tower' dimension.\n", + " grad = tf.concat(grads, 0)\n", + " grad = tf.reduce_mean(grad, 0)\n", + "\n", + " # Keep in mind that the Variables are redundant because they are shared\n", + " # across towers. So .. we will just return the first tower's pointer to\n", + " # the Variable.\n", + " v = grad_and_vars[0][1]\n", + " grad_and_var = (grad, v)\n", + " average_grads.append(grad_and_var)\n", + " return average_grads\n", + "\n", + "print('Done')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done\n" + ] + } + ], + "source": [ + "# wire everything up\n", + "# By default, all variables will be placed on '/gpu:0'\n", + "# So we need a custom device function, to assign all variables to '/cpu:0'\n", + "# Note: If GPUs are peered, '/gpu:0' can be a faster option\n", + "PS_OPS = ['Variable', 'VariableV2', 'AutoReloadVariable']\n", + "\n", + "def assign_to_device(device, ps_device='/cpu:0'):\n", + " def _assign(op):\n", + " node_def = op if isinstance(op, tf.NodeDef) else op.node_def\n", + " if node_def.op in PS_OPS:\n", + " return \"/\" + ps_device\n", + " else:\n", + " return device\n", + "\n", + " return _assign\n", + "\n", + "print('Done')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :37: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "\n", + "Future major versions of TensorFlow will allow gradients to flow\n", + "into the labels input on backprop by default.\n", + "\n", + "See tf.nn.softmax_cross_entropy_with_logits_v2.\n", + "\n", + "GPU 0 configured\n", + "GPU 1 configured\n", + "Step 1: Minibatch Loss= 0.6962, Training Accuracy= 0.600, 143 Examples/sec\n", + "Step 100: Minibatch Loss= 0.5726, Training Accuracy= 0.810, 14454 Examples/sec\n", + "Step 200: Minibatch Loss= 0.5195, Training Accuracy= 0.890, 13153 Examples/sec\n", + "Step 300: Minibatch Loss= 0.4557, Training Accuracy= 0.940, 12475 Examples/sec\n", + "Step 400: Minibatch Loss= 0.4271, Training Accuracy= 0.950, 13539 Examples/sec\n", + "Step 500: Minibatch Loss= 0.4394, Training Accuracy= 0.930, 13104 Examples/sec\n", + "Step 600: Minibatch Loss= 0.3987, Training Accuracy= 0.970, 13046 Examples/sec\n", + "Step 700: Minibatch Loss= 0.4068, Training Accuracy= 0.940, 13251 Examples/sec\n", + "Step 800: Minibatch Loss= 0.3721, Training Accuracy= 0.930, 13309 Examples/sec\n", + "Step 900: Minibatch Loss= 0.3927, Training Accuracy= 0.980, 13131 Examples/sec\n", + "Step 1000: Minibatch Loss= 0.3614, Training Accuracy= 0.960, 13528 Examples/sec\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.9358\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "tf.reset_default_graph()\n", + "\n", + "NUM_STEPS=1000\n", + "\n", + "# Place all ops on CPU by default\n", + "with tf.device('/cpu:0'):\n", + " tower_grads = []\n", + " reuse_vars = False\n", + " #reuse_vars = True\n", + " num_samples = X_train.shape[0]\n", + "\n", + " # tf Graph input\n", + " X = tf.placeholder(tf.float32, [None, VOCAB_SIZE])\n", + " Y = tf.placeholder(tf.float32, [None, NUM_LABELS])\n", + "\n", + " # Loop over all GPUs and construct their own computation graph\n", + " for i in range(NUM_GPUS):\n", + " with tf.device(assign_to_device('/gpu:{}'.format(i), ps_device='/cpu:0')):\n", + "\n", + " # Split data between GPUs\n", + " _x = X[i * BATCH_SIZE: (i+1) * BATCH_SIZE]\n", + " _y = Y[i * BATCH_SIZE: (i+1) * BATCH_SIZE]\n", + "\n", + " # Because Dropout have different behavior at training and prediction time, we\n", + " # need to create 2 distinct computation graphs that share the same weights.\n", + "\n", + " # Create a graph for training\n", + " logits_train = model(_x, reuse=reuse_vars, prefix=\"yelp\")\n", + " \n", + " # Create another graph for testing that reuse the same weights\n", + " logits_test = model(_x, reuse=True, prefix=\"yelp\", is_training=False)\n", + "\n", + " # Define loss and optimizer (with train logits, for dropout to take effect)\n", + " loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=_y))\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)\n", + " grads = optimizer.compute_gradients(loss_op)\n", + " \n", + " print(\"GPU\",i,\"configured\")\n", + " \n", + " # Only first GPU compute accuracy\n", + " if i == 0:\n", + " # Evaluate model (with test logits, for dropout to be disabled)\n", + " correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(_y, 1))\n", + " accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + " reuse_vars = True\n", + " tower_grads.append(grads)\n", + "\n", + " tower_grads = average_gradients(tower_grads)\n", + " train_op = optimizer.apply_gradients(tower_grads)\n", + " \n", + " # Initializing the variables\n", + " init = tf.global_variables_initializer()\n", + "\n", + " # Launch the graph\n", + " with tf.Session() as sess:\n", + " sess.run(init)\n", + " step = 1\n", + " # Keep training until reach max iterations\n", + " for step in range(1, NUM_STEPS + 1):\n", + " # Get a batch for each GPU\n", + " indices = np.random.choice(num_samples, BATCH_SIZE*NUM_GPUS)\n", + " batch_x = X_train[indices]\n", + " batch_y = y_train[indices]\n", + " \n", + " # Run optimization op (backprop)\n", + " ts = time.time()\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " te = time.time() - ts\n", + " if step % DISPLAY_STEP == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \": Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc) + \", %i Examples/sec\" % int(len(batch_x)/te))\n", + " step += 1\n", + " print(\"Optimization Finished!\")\n", + " \n", + " graph = tf.get_default_graph()\n", + " t1 = graph.get_tensor_by_name('Model/yelp_embedding/kernel:0')\n", + " embeddings = np.array(sess.run(t1))\n", + "\n", + " # Calculate accuracy for 1000 mnist test images\n", + " print(\"Testing Accuracy:\", \\\n", + " np.mean([sess.run(accuracy, feed_dict={X: X_test[i:i+BATCH_SIZE],\n", + " Y: y_test[i:i+BATCH_SIZE]}) for i in range(0, X_test.shape[0], BATCH_SIZE)]))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('amazing', 4.0019403),\n", + " ('delicious', 3.989573),\n", + " ('awesome', 3.978746),\n", + " ('excellent', 3.960814),\n", + " ('great', 3.8401873),\n", + " ('perfect', 3.6464357),\n", + " ('fantastic', 3.565358),\n", + " ('love', 3.2784226),\n", + " ('best', 3.2371793),\n", + " ('thank', 3.1843255)]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_most_similar_words(focus = \"love\"):\n", + " keys = word2index.keys()\n", + " if focus not in keys:\n", + " print(\"Sorry, word not found\")\n", + " return\n", + " \n", + " most_similar = Counter()\n", + " for word in word2index.keys():\n", + " most_similar[word] = np.dot(embeddings[word2index[word]],\n", + " embeddings[word2index[focus]])\n", + " \n", + " return most_similar.most_common()[0:10]\n", + "\n", + "get_most_similar_words('yummy')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.colors as colors\n", + "\n", + "words_to_visualize = list()\n", + "for word, ratio in pos_neg_ratios.most_common(500):\n", + " if(word in word2index.keys()):\n", + " words_to_visualize.append(word)\n", + " \n", + "for word, ratio in list(reversed(pos_neg_ratios.most_common()))[0:500]:\n", + " if(word in word2index.keys()):\n", + " words_to_visualize.append(word)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pos 500 neg 500\n" + ] + } + ], + "source": [ + "pos = 0\n", + "neg = 0\n", + "\n", + "colors_list = list()\n", + "vectors_list = list()\n", + "for word in words_to_visualize:\n", + " if word in pos_neg_ratios.keys():\n", + " vectors_list.append(embeddings[word2index[word]])\n", + " if(pos_neg_ratios[word] > 0):\n", + " pos+=1\n", + " colors_list.append(\"#00ff00\")\n", + " else:\n", + " neg+=1\n", + " colors_list.append(\"#000000\")\n", + " \n", + "print(\"Pos\",pos,\"neg\",neg)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TSNE visualization ready\n" + ] + } + ], + "source": [ + "from sklearn.manifold import TSNE\n", + "tsne = TSNE(n_components=2, random_state=0)\n", + "words_top_ted_tsne = tsne.fit_transform(vectors_list)\n", + "print(\"TSNE visualization ready\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " \n", + " var docs_json = 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\",\"dtype\":\"float32\",\"shape\":[1000]}},\"selected\":{\"id\":\"abef5fd0-5c9f-4afe-9c15-9fa63cd010f3\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"eb99db9a-e31c-4c6c-a248-4979fd20a806\",\"type\":\"UnionRenderers\"}},\"id\":\"78f0759e-01c6-4437-8b2d-a1018d3251a3\",\"type\":\"ColumnDataSource\"}],\"root_ids\":[\"1590e92e-6015-43b3-a675-a16e96426141\"]},\"title\":\"Bokeh Application\",\"version\":\"0.13.0\"}};\n", + " var render_items = [{\"docid\":\"05ec4ef3-b65f-4ba0-a5c4-c6a64e3ded5b\",\"roots\":{\"1590e92e-6015-43b3-a675-a16e96426141\":\"deba4cf8-d90c-431c-a2bc-e2c817dfd9e3\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " clearInterval(timer);\n", + " }\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", + " clearInterval(timer);\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1590e92e-6015-43b3-a675-a16e96426141" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", + " toolbar_location=\"above\",\n", + " title=\"vector T-SNE for most polarized words\")\n", + "\n", + "source = ColumnDataSource(data=dict(x1=words_top_ted_tsne[:,0],\n", + " x2=words_top_ted_tsne[:,1],\n", + " names=words_to_visualize,\n", + " color=colors_list))\n", + "\n", + "p.scatter(x=\"x1\", y=\"x2\", size=8, source=source, fill_color=\"color\")\n", + "\n", + "word_labels = LabelSet(x=\"x1\", y=\"x2\", text=\"names\", y_offset=6,\n", + " text_font_size=\"8pt\", text_color=\"#555555\",\n", + " source=source, text_align='center')\n", + "p.add_layout(word_labels)\n", + "\n", + "show(p)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}