add plot to eval script
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@ -1,7 +1,9 @@
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import csv
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import os
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import sys
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import pandas as pd
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import tensorflow as tf
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import matplotlib.pyplot as plt
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cwd = os.path.abspath(os.path.dirname(sys.argv[0]))
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modelPath = 'MyModel_tf'
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@ -24,7 +26,10 @@ new_model = tf.keras.models.load_model(modelPath)
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# Evaluate the restored model
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loss = new_model.evaluate(house_price_test_features, house_price_test_expected, verbose=2)
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print(loss)
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print("------\n")
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print(f"loss result: ${loss}\n")
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print("------")
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#print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))
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count = 0
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@ -36,3 +41,20 @@ except:
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with open('trainResults.csv', 'a+') as trainResults:
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trainResults.write(f"{count},{loss}" + "\n")
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try:
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x = []
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y = []
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with open('trainResults.csv', 'r') as trainResults:
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plots = csv.reader(trainResults, delimiter = ',')
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for row in plots:
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x.append(row[0])
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y.append(row[1])
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plt.bar(x, y, color = 'g', label = "loss")
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plt.xlabel('builds')
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plt.ylabel('losses')
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plt.title('loss for build')
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plt.legend()
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plt.show()
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except:
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pass
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