CleanPegasus

# Run this code on the terminal
# Make sure you have curl installed. If not use "sudo apt install curl" to install it
echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list && \
curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add -
sudo apt update
sudo apt-get install tensorflow-model-server

# Or alternatively you can use pip to install tensorflow serving
# If you don't have pip installed use "sudo apt install python3-pip" to install it in your linux environment
pip3 install tensorflow-serving-api

CleanPegasus

import tempfile

model_dir = tempfile.gettempdir() # gets the path of the temporary folder
version = 1 # version of the specific model
export_path = os.path.join(model_dir, str(version))
if os.path.isdir(export_path):
  print("Found an existing version of the model. Deleting the previous version\n")
  !rm -r {export_path} # terminal command to remove a directory

# Saving the model

CleanPegasus

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf

from tensorflow.keras.layers import Conv2D, GlobalAveragePooling2D, MaxPooling2D, SeparableConv2D
from tensorflow.keras.layers import Dense, Input, Dropout, BatchNormalization, Activation 
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.regularizers import l2

CleanPegasus

os.environ['MODEL_DIR'] = model_dir

CleanPegasus

# The model will be accessible as an API through the port 8501
bash --bg 
nohup tensorflow_model_server \
  --rest_api_port = 8501 \
  --model_name = emotion_recognizer \
  --model_base_path = "${MODEL_DIR}" >server.log 2>&1

CleanPegasus

img = cv2.imread('img.jpg') # reading the image
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # converting the image to grayscale
img =  cv2.resize(img, (48, 48)) # resizing the image
img = img/255 # normalizing the image
img = np.reshape(img, [-1, 48, 48, 1]) # reshaping the image to be suitable for serving

CleanPegasus

import requests
import json

EMOTIONS = ["angry", "disgust", "scared", "happy", "sad", "surprised", "neutral"] # Emotions corresponding to the output
data = json.dumps({"signature_name": "serving_default", "instances": img.tolist()}) # making the input in the format required for serving

headers = {"content-type": "application/json"} # specifying that the input will be in json format
json_response = requests.post('http://localhost:8501/v1/models/emotion_model:predict', data=data, headers=headers) # post request to the served model
predictions = json.loads(json_response.text)['predictions'] # getting the predictions

CleanPegasus

import tensorflow as tf

def sample(x, y, z):
  return tf.reduce_sum(x + y * z)

CleanPegasus

from tensordash.tensordash import Tensordash

histories = Tensordash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')
    
try:
    model.fit(
    X_train, 

CleanPegasus

from tensordash.torchdash import Torchdash

histories = Torchdash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')

try:
    for epoch in range(epochs):
        losses = []