52 lines
No EOL
1.3 KiB
Python
52 lines
No EOL
1.3 KiB
Python
#Import libaries and datasets
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import matplotlib.pyplot as plt
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import numpy as np
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import tensorflow as tf
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import pandas as pd
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import seaborn as sns
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import pickle
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import random
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import os
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with open(os.path.join("dataset", "train.p"), mode='rb') as training_data:
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train = pickle.load(training_data)
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with open(os.path.join("dataset", "valid.p"), mode='rb') as validation_data:
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valid = pickle.load(validation_data)
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X_train, y_train = train['features'], train['labels']
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X_valid, y_valid = valid['features'], valid['labels']
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from sklearn.utils import shuffle
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X_train, y_train = shuffle(X_train, y_train)
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X_valid, y_valid = shuffle(X_valid, y_valid)
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# Normalize image to [0, 1]
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X_train_norm = X_train / 255
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X_valid_norm = X_valid / 255
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mask = np.isin(y_valid, range(20))
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X_valid_subset = X_valid_norm[mask]
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y_valid_subset = y_valid[mask]
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#convolutionalNeuralNetwork
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#fullyConnectedNeuralNetwork
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model = tf.keras.models.load_model('saved_model/convolutionalNeuralNetwork.h5')
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score = model.evaluate(X_valid_subset, y_valid_subset)
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print('Test Accuracy: {}'.format(score[1]))
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from sklearn.metrics import confusion_matrix
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predicted_classes = np.argmax(model.predict(X_valid_subset), axis=-1)
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y_true = y_valid_subset
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cm = confusion_matrix(y_true, predicted_classes)
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plt.figure(figsize = (25, 25))
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sns.heatmap(cm, annot = True)
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plt.show() |