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Trained the model and attatched the results#89
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Aksh4y2604 wants to merge 2 commits intoUWARG:masterfrom
Open
Trained the model and attatched the results#89Aksh4y2604 wants to merge 2 commits intoUWARG:masterfrom
Aksh4y2604 wants to merge 2 commits intoUWARG:masterfrom
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rayjinghaolei
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May 27, 2022
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rayjinghaolei
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all good in terms of other aspects
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| model = models.Sequential() | ||
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| #Defining the convulational a stack of COnv2D and MaxPolling2D layers | ||
| model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) | ||
| model.add(layers.MaxPooling2D((2, 2))) | ||
| model.add(layers.Conv2D(64, (3, 3), activation='relu')) | ||
| model.add(layers.MaxPooling2D((2, 2))) | ||
| model.add(layers.Conv2D(64, (3, 3), activation='relu')) | ||
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| #Displaying the model architecture | ||
| print("Before Flattening: ") | ||
| model.summary() | ||
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| model.add(layers.Flatten()) | ||
| model.add(layers.Dense(64, activation='relu')) | ||
| model.add(layers.Dense(10)) | ||
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| print("After Flattening: ") | ||
| model.summary() | ||
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| #Compiling and training the model | ||
| model.compile(optimizer='adam', | ||
| loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), | ||
| metrics=['accuracy']) | ||
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| history = model.fit(trainImages, trainLables, epochs=10, | ||
| validation_data=(testImages, testLables)) |
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I think you can group this into a function as well
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| plt.plot(history.history['accuracy'], label='accuracy') | ||
| plt.plot(history.history['val_accuracy'], label = 'val_accuracy') | ||
| plt.xlabel('Epoch') | ||
| plt.ylabel('Accuracy') | ||
| plt.ylim([0.5, 1]) | ||
| plt.legend(loc='lower right') |
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also group this into the function
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| print("Test Loss: ", testLoss) | ||
| print("Test Accuracy: ", testAccuracy) | ||
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the idea is to group most of the code into functions and to have main function calling them one by one
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3 participants
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