dipanjanS

# get some sample data
sample_test_data = test_images[580:590]
sample_test_labels = test_labels[580:590]

# pre-process data
IMG_DIMS = (32, 32)
sample_test_data_processed = (np.array([resize_image_array(img, 
                                                           img_size_dims=IMG_DIMS) 
                                            for img in np.stack([sample_test_data]*3, 
                                                                axis=-1)])) / 255.

dipanjanS

# get some sample data
sample_test_data = test_images[580:590]
sample_test_labels = test_labels[580:590]

# pre-process data 
sample_test_data_processed = np.expand_dims(sample_test_data / 255., axis=3)

# create payload
data = json.dumps({"signature_name": "serving_default", 
                   "instances": sample_test_data_processed.tolist()})

dipanjanS

%%bash --bg 
nohup /usr/bin/tensorflow_model_server \
  --rest_api_port=8501 \
  --model_config_file="/home/jupyter/tensorflow_serving/models.conf" >server.log 2>&1

# checking the logs
!tail server.log

dipanjanS

# saving model 1
model1 = create_cnn_architecture_model1(input_shape=INPUT_SHAPE)
model1.load_weights('./model_weights/cnn_model1_wt.h5')
export_path = './tf_saved_models/1'

tf.saved_model.simple_save(
    keras.backend.get_session(),
    export_path,
    inputs={'input_image': model1.input},
    outputs={t.name:t for t in model1.outputs})

dipanjanS

# save model
if not os.path.isdir('model_weights/'):
    os.mkdir('model_weights/')
model2.save_weights(filepath='model_weights/cnn_model2_wt.h5', overwrite=True)

# load model (can be used in the future as needed once trained)
model2 = create_cnn_architecture_model2(input_shape=INPUT_SHAPE_RN)
model2.load_weights('model_weights/cnn_model2_wt.h5')

# predict and evaluate on test dataset

dipanjanS

EPOCHS = 10
train_images_3ch_scaled = train_images_3ch / 255.
model2.fit(train_images_3ch_scaled, train_labels, validation_split=0.1, 
           epochs=EPOCHS)

# Output
Train on 54000 samples, validate on 6000 samples
Epoch 1/10
54000/54000 [====] - 100s 2ms/sample - loss: 0.5434 - acc: 0.8299 - val_loss: 0.3318 - val_acc: 0.9043
Epoch 2/10

dipanjanS

import cv2

def resize_image_array(img, img_size_dims):
    img = cv2.resize(img, dsize=img_size_dims, 
                     interpolation=cv2.INTER_CUBIC)
    img = np.array(img, dtype=np.float32)
    return img

# convert single channel images to 3-channel images    
train_images_3ch = np.stack([train_images]*3, axis=-1)

dipanjanS

INPUT_SHAPE_RN = (32, 32, 3)

def create_cnn_architecture_model2(input_shape):
    inc_net = keras.applications.resnet50.ResNet50(include_top=False, weights='imagenet', 
                                                          input_shape=input_shape)
    inc_net.trainable = True
    # Fine-tune the layers
    for layer in inc_net.layers:
        layer.trainable = True
    

dipanjanS

import os
from sklearn.metrics import confusion_matrix, classification_report
import pandas as pd

# save model
if not os.path.isdir('model_weights/'):
    os.mkdir('model_weights/')
model.save_weights(filepath='model_weights/cnn_model1_wt.h5', overwrite=True)

# load model (can be used in the future as needed once trained)

dipanjanS

EPOCHS = 10
train_images_scaled = train_images_gr / 255.
model.fit(train_images_scaled, train_labels, validation_split=0.1, epochs=EPOCHS)

# Output
Train on 54000 samples, validate on 6000 samples
Epoch 1/10
54000/54000 [====] - 7s 122us/sample - loss: 0.4614 - acc: 0.8323 - val_loss: 0.3462 - val_acc: 0.8725
Epoch 2/10
54000/54000 [====] - 5s 86us/sample - loss: 0.3073 - acc: 0.8892 - val_loss: 0.2825 - val_acc: 0.8950