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import matplotlib.pyplot as plt

# Plot utility
def plot_graphs(history, string):
  plt.plot(history.history[string])
  plt.plot(history.history['val_'+string])
  plt.xlabel("Epochs")
  plt.ylabel(string)
  plt.legend([string, 'val_'+string])
  plt.show()

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algonacci

import matplotlib.pyplot as plt

plt.rcParams.update({
    "lines.color": "white",
    "patch.edgecolor": "white",
    "text.color": "white",
    "axes.facecolor": "black",
    "axes.edgecolor": "white",
    "axes.labelcolor": "white",
    "xtick.color": "white",

algonacci

<?php

namespace App\Http\Controllers\API;

class ResponseFormatter
{
  protected static $response = [
    'meta' => [
      'code' => 200,
      'status' => 'success',

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import pandas as pd

evaluation = pd.DataFrame(model.history.history)
evaluation[['accuracy', 'val_accuracy']].plot()
evaluation[['loss', 'val_loss']].plot()

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# Plot the training and validation accuracies for each epoch

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

fig = plt.figure(figsize = (15,5))

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class myCallback(tf.keras.callbacks.Callback):
        def on_epoch_end(self, epoch, logs={}):
            if logs.get('accuracy') is not None and logs.get('val_accuracy') > 0.85:
                print("\nReached 85% validation accuracy so cancelling training!")
                self.model.stop_training = True
              
callback = myCallback()

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import requests
from bs4 import BeautifulSoup as soup
import os

# Define Website to Download pdf
url = 'website to download pdfs'

# Get Website content
r = requests.get(url)

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gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
  print('Not connected to a GPU')
else:
  print(gpu_info)
  
from psutil import virtual_memory
ram_gb = virtual_memory().total / 1e9
print('Your runtime has {:.1f} gigabytes of available RAM\n'.format(ram_gb))