securetorobert

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(predictors, target, test_size=0.3, random_state=40)

securetorobert

predictors = train_df[['rm']]
target = train_df['medv']

securetorobert

import pandas as pd
train_df = pd.read_csv('/content/gdrive/My Drive/boston/train.csv', index_col='ID')

securetorobert

from google.colab import drive
drive.mount('/content/gdrive')

securetorobert

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
  labels:
    app: nginx
spec:
  replicas: 3
  selector:
    matchLabels:

securetorobert

// Imports the Google Cloud client library
const {Translate} = require('@google-cloud/translate').v2;

// Creates a client
const translate = new Translate();

/**
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const text = 'The text to translate, e.g. Hello, world!';

securetorobert

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(ds, epochs=3, steps_per_epoch=10)

securetorobert

def preprocess_image(image):
  image = tf.image.decode_jpeg(image, channels=NUM_CHANNELS)
  image = tf.image.resize(image, [HEIGHT, WIDTH])
  image /= 255.0  # normalize to [0,1] range

  return image

def load_and_preprocess_image(path):
  image = tf.io.read_file(path)
  return preprocess_image(image)

securetorobert

x = base_model.output
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(4096, activation='relu')(x)
x = layers.Dense(1, activation='sigmoid')(x)

model_3 = models.Model(inputs=base_model.input, outputs=x)
print(model_3.summary())

securetorobert

base_model = tf.keras.applications.vgg19.VGG19(input_shape=(HEIGHT, WIDTH, NUM_CHANNELS), include_top=False, weights='imagenet')
base_model.trainable = False

print(base_model.summary())