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plt.figure(figsize: [12, 8])

let accuracyAxes = plt.subplot(2, 1, 1)
accuracyAxes.set_ylabel("Train Accuracy")
accuracyAxes.plot(trainAccuracyResults, color: "blue")

let lossAxes = plt.subplot(2, 1, 2)
lossAxes.set_ylabel("Test Accuracy")
lossAxes.set_xlabel("Epoch")
lossAxes.plot(testAccuracyResults, color: "yellow")

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print("Beginning training...")

struct Statistics {
    var correctGuessCount: Int = 0
    var totalGuessCount: Int = 0
    var totalLoss: Float = 0
}

// Store accuracy results during training
var trainAccuracyResults: [Float] = []

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// Using Gradient Descent as the optimizer
let optimizer = SGD(for: classifier, learningRate: 0.1)

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import TensorFlow

let epochCount = 100
let batchSize = 128

// The LeNet-5 model
var classifier = Sequential {
    Conv2D<Float>(filterShape: (5, 5, 1, 6), padding: .same, activation: relu)
    AvgPool2D<Float>(poolSize: (2, 2), strides: (2, 2))
    Conv2D<Float>(filterShape: (5, 5, 6, 16), activation: relu)

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# Display first 5 images
for img in imgs{
  plt.imshow(img.reshape(28,28))
  plt.show()
}

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%include "/content/swift-datascience/MNIST.swift"

// Load dataset
let dataset = MNIST(batchSize: 128)

// Get first 5 images
let imgs = dataset.trainingImages.minibatch(at: 0, batchSize: 5).makeNumpyArray()
print(imgs.shape)

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%include "EnableIPythonDisplay.swift"
IPythonDisplay.shell.enable_matplotlib("inline")

import Foundation
import Python

let os = Python.import("os")
let plt = Python.import("matplotlib.pyplot")

os.system("git clone https://github.com/mohdsanadzakirizvi/swift-datascience.git")

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from sklearn.metrics import accuracy_score

print(accuracy_score(y_val, pred_bert))

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from sklearn.linear_model import LogisticRegression

# LR model
model_bert = LogisticRegression()
# train
model_bert = model_bert.fit(X_tr_bert, y_tr)
# predict
pred_bert = model_bert.predict(X_val_bert)

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from bert_serving.client import BertClient

# make a connection with the BERT server using it's ip address
bc = BertClient(ip="YOUR_SERVER_IP")
# get the embedding for train and val sets
X_tr_bert = bc.encode(X_tr.tolist())
X_val_bert = bc.encode(X_val.tolist())