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utils.py
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utils.py
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import numpy as np
import matplotlib.pyplot as plt
import itertools
def tensorflow_to_numpy(ds, model):
"""
Iterate over a tensorflow dataset object to get the predicted class and the true class as numpy objects
@params
ds: dataset object
@return
true_classes: true classses
predicted_classes: predicted classes
"""
predicted_classes = np.array([])
true_classes = np.array([])
for x, y in ds:
predicted_classes = np.concatenate([predicted_classes,
np.argmax(model(x), axis = -1)])
true_classes = np.concatenate([true_classes, np.argmax(y.numpy(), axis=-1)])
return true_classes, predicted_classes
def plotComplexity(history, plot_accuracy=False):
if plot_accuracy == True:
train_loss = history.history["loss"]
val_loss = history.history["val_loss"]
train_accuracy = history.history['categorical_accuracy']
val_accuracy = history.history['val_categorical_accuracy']
fig, axes = plt.subplots(2, 1, figsize=(14,10))
axes[0].plot(train_loss, label="Train Loss")
axes[0].plot(val_loss, label="Validation Loss")
axes[0].legend()
axes[0].set_title("Train Loss vs Validation Loss")
axes[1].plot(train_accuracy, label='Train Accuracy')
axes[1].plot(val_accuracy, label='Validation Accuracy')
axes[1].legend()
axes[1].set_title('Train Accuracy vs Validation Accuracy')
plt.show()
elif plot_accuracy == False:
train_loss = history.history["loss"]
val_loss = history.history["val_loss"]
plt.plot(train_loss, label='Train Loss')
plt.plot(val_loss, label='Val Loss')
plt.legend()
plt.title('Train Loss vs Validation Loss')
plt.show()
def plot_cm(cm, class_names):
figure = plt.figure(figsize=(8, 8))
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title("Confusion matrix")
plt.colorbar()
tick_marks = np.arange(len(class_names))
plt.xticks(tick_marks, class_names, rotation=45)
plt.yticks(tick_marks, class_names)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], '.2f'), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
return figure