Imports¶
In [49]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import math
from skimage.feature import hog
In [50]:
import warnings
warnings.filterwarnings('ignore')
Fetching and analysing the data¶
Minimum number of faces per person selected = 70
Number of labels = 7
Number of images = 1288
In [51]:
from sklearn.datasets import fetch_lfw_people
# Download and load the LFW dataset
lfw_dataset = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
# Extract data and target labels
X = lfw_dataset.images
y = lfw_dataset.target
target_names = lfw_dataset.target_names
# Print information about the dataset
print("Number of images:", X.shape[0])
print("Image size:", X.shape[1:])
print("Number of unique classes:", len(target_names))
print("Target class names:", target_names)
num_samples, num_features = lfw_dataset.data.shape
num_classes = len(target_names)
Number of images: 1288 Image size: (50, 37) Number of unique classes: 7 Target class names: ['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush' 'Gerhard Schroeder' 'Hugo Chavez' 'Tony Blair']
Sample images
In [52]:
num_samples_to_display = 5
random_indices = np.random.choice(num_samples, num_samples_to_display, replace=False)
plt.figure(figsize=(10, 4))
for i, index in enumerate(random_indices, 1):
plt.subplot(1, num_samples_to_display, i)
plt.imshow(X[index].reshape((50, 37)), cmap='gray')
plt.title(target_names[y[index]])
plt.axis('off')
plt.show()
Distribution of classes
In [53]:
class_counts = np.bincount(y)
plt.bar(range(num_classes), class_counts)
plt.xlabel('Class Index')
plt.ylabel('Number of Samples')
plt.title('Distribution of Classes')
plt.xticks(range(num_classes), target_names, rotation=90)
plt.show()
Analysing how each image data looks like
In [54]:
print('shape of each image data: ',lfw_dataset.images[0].shape)
print(lfw_dataset.images[0])
plt.axis("off")
plt.imshow(lfw_dataset.images[0], cmap='gray')
shape of each image data: (50, 37) [[0.9973857 0.99607843 0.9921568 ... 0.29803923 0.24836601 0.20653595] [0.9973857 0.9921569 0.9908497 ... 0.30588236 0.2535948 0.21568628] [0.96078426 0.93071896 0.8679738 ... 0.2875817 0.24183007 0.21568628] ... [0.34509805 0.26143792 0.17385621 ... 0.4248366 0.40261438 0.39084968] [0.30980393 0.23398693 0.17124183 ... 0.39869282 0.4013072 0.3764706 ] [0.28366014 0.2248366 0.18039216 ... 0.38169935 0.38823533 0.3803922 ]]
Out[54]:
<matplotlib.image.AxesImage at 0x7948f018c4c0>
Reshaping the data into single array of 1850 (50 * 37) features
In [55]:
print('shape of each image data: ',lfw_dataset.data[0].shape)
print(lfw_dataset.data[0])
shape of each image data: (1850,) [0.9973857 0.99607843 0.9921568 ... 0.38169935 0.38823533 0.3803922 ]
Implementing Different Feature Extraction Techniques from Scratch¶
- Linear Binary Pattern [LBP]
- Histogram of Oriented Gradients [HoG]
- Convolutional Neural Networks [CNN]
Linear Binary Pattern¶
From Scratch implementation
In [56]:
def lbp_features(image):
lbp_image = np.ones([50,37])
for y in range(1,image.shape[0]-1):
for x in range(1,image.shape[1]-1):
l = [*list(image[y-1][x-1:x+2]),image[y][x+1],*list(image[y+1][x+1:x-1:-1]),image[y+1][x-1],image[y][x-1]]
mwl= [2**i if image[y][x] >= l[i] else 0 for i in range(len(l))]
lbp_image[y][x] = sum(mwl)
return lbp_image
Original Image
In [57]:
plt.axis("off")
plt.title('Original')
plt.imshow(lfw_dataset.images[163],cmap='gray')
Out[57]:
<matplotlib.image.AxesImage at 0x7948ecff51e0>
LBP Feature Extracted Image
In [58]:
a = lbp_features(lfw_dataset.images[163])
plt.axis("off")
plt.imshow(a,cmap='gray')
Out[58]:
<matplotlib.image.AxesImage at 0x7948ecff5b10>
LBP using sklearn local_binary_pattern function
In [59]:
from skimage.feature import local_binary_pattern
radius = 1
num_points = 8 * radius
lbp_image = local_binary_pattern(lfw_dataset.images[163], num_points, radius, method='uniform')
plt.axis("off")
plt.imshow(lbp_image.astype(np.uint8), cmap= 'gray')
Out[59]:
<matplotlib.image.AxesImage at 0x7948f018c5b0>
Histogram of Oriented Gradients¶
Resizing Image to 64 X 128 size
In [60]:
img = cv2.resize(lfw_dataset.images[163], (64, 128))
plt.axis("off")
plt.imshow(img, cmap='gray')
Out[60]:
<matplotlib.image.AxesImage at 0x7948e6680cd0>
Vertical Gradient Detection
In [61]:
def img_gradient_ver(image):
lbp_image = np.ones(image.shape)
for y in range(0,image.shape[0]):
for x in range(1,image.shape[1]-1):
lbp_image[y][x] = image[y][x+1]-image[y][x-1]
return lbp_image
In [62]:
a = img_gradient_ver(img)
plt.axis("off")
plt.imshow(a,cmap='gray')
Out[62]:
<matplotlib.image.AxesImage at 0x7948e9f3bcd0>
Horizontal Gradient Detection
In [63]:
def img_gradient_horr(image):
lbp_image = np.ones(image.shape)
for y in range(1,image.shape[0]-1):
for x in range(0,image.shape[1]):
lbp_image[y][x] = image[y+1][x]-image[y-1][x]
return lbp_image
In [64]:
b = img_gradient_horr(img)
plt.axis("off")
plt.imshow(b,cmap='gray')
Out[64]:
<matplotlib.image.AxesImage at 0x79497ff07040>
Adding both vertical and horizontal gradients
In [65]:
plt.axis("off")
plt.imshow(a+b,cmap='gray')
Out[65]:
<matplotlib.image.AxesImage at 0x7948e4526d70>
Gradient and Angle of image
In [66]:
mag = []
theta = []
for i in range(128):
magnitudeArray = []
angleArray = []
for j in range(64):
if j-1 <= 0 or j+1 >= 64:
if j-1 <= 0:
Gx = img[i][j+1] - 0
elif j + 1 >= len(img[0]):
Gx = 0 - img[i][j-1]
else:
Gx = img[i][j+1] - img[i][j-1]
if i-1 <= 0 or i+1 >= 128:
if i-1 <= 0:
Gy = 0 - img[i+1][j]
elif i +1 >= 128:
Gy = img[i-1][j] - 0
else:
Gy = img[i-1][j] - img[i+1][j]
magnitude = math.sqrt(pow(Gx, 2) + pow(Gy, 2)) # Calculating magnitude
magnitudeArray.append(round(magnitude, 9))
if Gx == 0: # Calculating angle
angle = math.degrees(0.0)
else:
angle = math.degrees(abs(math.atan(Gy / Gx)))
angleArray.append(round(angle, 9))
mag.append(magnitudeArray)
theta.append(angleArray)
Represetation of magnitude of gradient of image
In [67]:
plt.imshow(mag, cmap="gray")
plt.axis("off")
plt.title('Magnitudes')
plt.show()
Representation of angle of image
In [68]:
plt.imshow(theta, cmap="gray")
plt.axis("off")
plt.title('Angles')
plt.show()
Final Histogram of Oriented Gradients Image
In [69]:
_ , hog_image = hog(img, orientations=8, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualize=True)
plt.axis('off')
plt.title('HoG')
plt.imshow(hog_image, cmap='gray')
Out[69]:
<matplotlib.image.AxesImage at 0x79497fdd9cc0>
Convolutional Layers¶
Convolution function from scratch
In [70]:
def convolutional_features(image, kernel):
output = np.zeros((image.shape[0] - kernel.shape[0] + 1, image.shape[1] - kernel.shape[1] + 1))
for i in range(output.shape[0]):
for j in range(output.shape[1]):
output[i, j] = np.multiply(image[i:i+kernel.shape[0],j:j+kernel.shape[1]],kernel).sum()
return output
3 different types of kernels
In [71]:
kernel_ver = np.array([[-1, 0, 1],
[-2,0,2],
[-1,0,1]])
kernel_hor = np.array([[-1, -2, -1],
[0,0,0],
[1,2,1]])
kernel_gabor = np.array([[-0.2324, -0.268, -0.2324],
[0.8673,1,0.8673],
[-0.2324,-0.268,-0.2324]])
Verrtical kernel for edge detection
In [72]:
d = convolutional_features(lfw_dataset.images[163], kernel_ver)
plt.axis('off')
plt.title('Vertical Edges')
plt.imshow(d, cmap='gray')
Out[72]:
<matplotlib.image.AxesImage at 0x7949801fcfa0>
Horizontal kernel for edge detection
In [73]:
e = convolutional_features(lfw_dataset.images[163], kernel_hor)
plt.title('Horizontal Edges')
plt.axis('off')
plt.imshow(e, cmap='gray')
Out[73]:
<matplotlib.image.AxesImage at 0x7948f014f130>
Gabor Filter Kernel
In [74]:
f = convolutional_features(lfw_dataset.images[163], kernel_gabor)
plt.title('Gabor')
plt.axis('off')
plt.imshow(f, cmap='gray')
Out[74]:
<matplotlib.image.AxesImage at 0x7948fcdc2920>
Using all 3 at the same time
In [75]:
plt.axis('off')
plt.imshow(d + e + f, cmap ='gray')
Out[75]:
<matplotlib.image.AxesImage at 0x7948fcd31240>
In [76]:
from sklearn.metrics import classification_report, accuracy_score, ConfusionMatrixDisplay
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.decomposition import PCA
from sklearn.model_selection import RandomizedSearchCV, train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
Trying PCA
In [77]:
X = lfw_dataset.data
y = lfw_dataset.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=69)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train) #scaling train and test data
X_test = scaler.transform(X_test)
pca = PCA(n_components = 10)
pca.fit(X_train)
print(pca.explained_variance_ratio_)
[0.255891 0.1450748 0.06833295 0.0516422 0.03171235 0.02718069 0.0204603 0.0168401 0.01583593 0.01506251]
Results with LBP¶
In [78]:
X_train = np.reshape(X_train, (X_train.shape[0], 50, 37))
X_test = np.reshape(X_test, (X_test.shape[0], 50, 37))
In [79]:
X_train_lbp = [lbp_features(x) for x in X_train]
X_test_lbp = [lbp_features(x) for x in X_test]
X_train_lbp = np.array(X_train_lbp)
X_test_lbp = np.array(X_test_lbp)
X_train_lbp = X_train_lbp.reshape((X_train.shape[0], 50*37))
X_test_lbp = X_test_lbp.reshape((X_test.shape[0], 50*37))
SVM
In [80]:
svm_lbp = SVC(kernel='rbf', decision_function_shape='ovo' )
svm_lbp.fit(X_train_lbp, y_train)
lbp_predictions = svm_lbp.predict(X_test_lbp)
print(classification_report(y_test, lbp_predictions, target_names = target_names))
ConfusionMatrixDisplay.from_estimator(svm_lbp, X_test_lbp, y_test, display_labels=target_names, xticks_rotation="vertical")
plt.tight_layout()
plt.show()
precision recall f1-score support
Ariel Sharon 0.00 0.00 0.00 12
Colin Powell 0.83 0.91 0.87 55
Donald Rumsfeld 1.00 0.14 0.25 21
George W Bush 0.62 0.98 0.76 106
Gerhard Schroeder 1.00 0.21 0.35 19
Hugo Chavez 0.00 0.00 0.00 15
Tony Blair 0.91 0.70 0.79 30
accuracy 0.71 258
macro avg 0.62 0.42 0.43 258
weighted avg 0.69 0.71 0.64 258
KNN
In [81]:
knn_lbp = KNeighborsClassifier(n_neighbors = 5)
knn_lbp.fit(X_train_lbp, y_train)
lbp_predictions = knn_lbp.predict(X_test_lbp)
print(classification_report(y_test, lbp_predictions, target_names = target_names))
ConfusionMatrixDisplay.from_estimator(knn_lbp, X_test_lbp, y_test, display_labels=target_names, xticks_rotation="vertical")
plt.tight_layout()
plt.show()
precision recall f1-score support
Ariel Sharon 0.80 0.67 0.73 12
Colin Powell 0.85 0.85 0.85 55
Donald Rumsfeld 0.93 0.67 0.78 21
George W Bush 0.80 0.96 0.87 106
Gerhard Schroeder 0.78 0.74 0.76 19
Hugo Chavez 0.71 0.33 0.45 15
Tony Blair 0.96 0.80 0.87 30
accuracy 0.83 258
macro avg 0.83 0.72 0.76 258
weighted avg 0.83 0.83 0.82 258
Decision Tree
In [82]:
tree_lbp = DecisionTreeClassifier()
tree_lbp.fit(X_train_lbp, y_train)
lbp_predictions = tree_lbp.predict(X_test_lbp)
print(classification_report(y_test, lbp_predictions, target_names = target_names))
ConfusionMatrixDisplay.from_estimator(tree_lbp, X_test_lbp, y_test, display_labels=target_names, xticks_rotation="vertical")
plt.tight_layout()
plt.show()
precision recall f1-score support
Ariel Sharon 0.12 0.17 0.14 12
Colin Powell 0.59 0.47 0.53 55
Donald Rumsfeld 0.10 0.10 0.10 21
George W Bush 0.61 0.58 0.60 106
Gerhard Schroeder 0.38 0.47 0.42 19
Hugo Chavez 0.10 0.13 0.11 15
Tony Blair 0.27 0.27 0.27 30
accuracy 0.43 258
macro avg 0.31 0.31 0.31 258
weighted avg 0.45 0.43 0.44 258
Results with HoG¶
In [83]:
X_train_hog = [hog_image for _ , hog_image in [hog(x, orientations=8, pixels_per_cell=(8, 8),cells_per_block=(2, 2), visualize=True) for x in X_train]]
X_test_hog = [hog_image for _ , hog_image in [hog(x, orientations=8, pixels_per_cell=(8, 8),cells_per_block=(2, 2), visualize=True) for x in X_test]]
X_train_hog = np.array(X_train_hog)
X_test_hog = np.array(X_test_hog)
X_train_hog = X_train_hog.reshape((X_train.shape[0], 50*37))
X_test_hog = X_test_hog.reshape((X_test.shape[0], 50*37))
SVM
In [84]:
svm_hog = SVC(kernel = 'rbf', decision_function_shape='ovo')
svm_hog.fit(X_train_hog, y_train)
hog_predictions = svm_hog.predict(X_test_hog)
print(classification_report(y_test, hog_predictions, target_names = target_names))
ConfusionMatrixDisplay.from_estimator(svm_hog, X_test_hog, y_test, display_labels=target_names, xticks_rotation="vertical")
plt.tight_layout()
plt.show()
precision recall f1-score support
Ariel Sharon 0.83 0.42 0.56 12
Colin Powell 0.86 0.89 0.88 55
Donald Rumsfeld 0.71 0.48 0.57 21
George W Bush 0.71 0.99 0.83 106
Gerhard Schroeder 0.70 0.37 0.48 19
Hugo Chavez 0.00 0.00 0.00 15
Tony Blair 0.87 0.67 0.75 30
accuracy 0.76 258
macro avg 0.67 0.54 0.58 258
weighted avg 0.72 0.76 0.72 258
KNN
In [85]:
knn_hog = KNeighborsClassifier(n_neighbors = 5)
knn_hog.fit(X_train_hog, y_train)
hog_predictions = knn_hog.predict(X_test_hog)
print(classification_report(y_test, hog_predictions, target_names = target_names))
ConfusionMatrixDisplay.from_estimator(knn_hog, X_test_hog, y_test, display_labels=target_names, xticks_rotation="vertical")
plt.tight_layout()
plt.show()
precision recall f1-score support
Ariel Sharon 0.55 0.50 0.52 12
Colin Powell 0.88 0.82 0.85 55
Donald Rumsfeld 0.54 0.67 0.60 21
George W Bush 0.78 0.94 0.85 106
Gerhard Schroeder 0.58 0.58 0.58 19
Hugo Chavez 0.75 0.20 0.32 15
Tony Blair 0.74 0.47 0.57 30
accuracy 0.75 258
macro avg 0.69 0.60 0.61 258
weighted avg 0.75 0.75 0.73 258
Decision Tree
In [86]:
tree_hog = DecisionTreeClassifier()
tree_hog.fit(X_train_hog, y_train)
hog_predictions = tree_hog.predict(X_test_hog)
print(classification_report(y_test, hog_predictions, target_names = target_names))
ConfusionMatrixDisplay.from_estimator(tree_hog, X_test_hog, y_test, display_labels=target_names, xticks_rotation="vertical")
plt.tight_layout()
plt.show()
precision recall f1-score support
Ariel Sharon 0.00 0.00 0.00 12
Colin Powell 0.56 0.58 0.57 55
Donald Rumsfeld 0.24 0.29 0.26 21
George W Bush 0.77 0.67 0.72 106
Gerhard Schroeder 0.13 0.16 0.14 19
Hugo Chavez 0.18 0.13 0.15 15
Tony Blair 0.30 0.37 0.33 30
accuracy 0.48 258
macro avg 0.31 0.31 0.31 258
weighted avg 0.51 0.48 0.50 258
Results with CNN¶
In [87]:
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from keras.utils import to_categorical
import keras
In [88]:
model = Sequential()
model.add(Conv2D(1, (3, 3), activation='relu', input_shape=(50, 37, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(1, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(np.unique(y)), activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
y_categorical = to_categorical(y_train, num_classes=len(np.unique(y)))
model.fit(X_train, y_categorical, epochs=50, batch_size=32, validation_split=0.2)
Epoch 1/50 26/26 [==============================] - 2s 41ms/step - loss: 2.0155 - accuracy: 0.2840 - val_loss: 1.6755 - val_accuracy: 0.4320 Epoch 2/50 26/26 [==============================] - 1s 19ms/step - loss: 1.7488 - accuracy: 0.3714 - val_loss: 1.6156 - val_accuracy: 0.4126 Epoch 3/50 26/26 [==============================] - 0s 18ms/step - loss: 1.6526 - accuracy: 0.4150 - val_loss: 1.5560 - val_accuracy: 0.4515 Epoch 4/50 26/26 [==============================] - 0s 19ms/step - loss: 1.5799 - accuracy: 0.4490 - val_loss: 1.4920 - val_accuracy: 0.4515 Epoch 5/50 26/26 [==============================] - 0s 18ms/step - loss: 1.5310 - accuracy: 0.4515 - val_loss: 1.4343 - val_accuracy: 0.5291 Epoch 6/50 26/26 [==============================] - 1s 19ms/step - loss: 1.4682 - accuracy: 0.4806 - val_loss: 1.3677 - val_accuracy: 0.5583 Epoch 7/50 26/26 [==============================] - 0s 18ms/step - loss: 1.3823 - accuracy: 0.5061 - val_loss: 1.3192 - val_accuracy: 0.5534 Epoch 8/50 26/26 [==============================] - 0s 19ms/step - loss: 1.3896 - accuracy: 0.4830 - val_loss: 1.2743 - val_accuracy: 0.5825 Epoch 9/50 26/26 [==============================] - 0s 18ms/step - loss: 1.3326 - accuracy: 0.5255 - val_loss: 1.2241 - val_accuracy: 0.5825 Epoch 10/50 26/26 [==============================] - 0s 19ms/step - loss: 1.2607 - accuracy: 0.5534 - val_loss: 1.1740 - val_accuracy: 0.5971 Epoch 11/50 26/26 [==============================] - 0s 18ms/step - loss: 1.2288 - accuracy: 0.5595 - val_loss: 1.1435 - val_accuracy: 0.6019 Epoch 12/50 26/26 [==============================] - 0s 19ms/step - loss: 1.1811 - accuracy: 0.5850 - val_loss: 1.1121 - val_accuracy: 0.5825 Epoch 13/50 26/26 [==============================] - 0s 19ms/step - loss: 1.1277 - accuracy: 0.5947 - val_loss: 1.0572 - val_accuracy: 0.6505 Epoch 14/50 26/26 [==============================] - 0s 18ms/step - loss: 1.1210 - accuracy: 0.5922 - val_loss: 1.0129 - val_accuracy: 0.6845 Epoch 15/50 26/26 [==============================] - 0s 18ms/step - loss: 1.0646 - accuracy: 0.6335 - val_loss: 1.0081 - val_accuracy: 0.6796 Epoch 16/50 26/26 [==============================] - 0s 18ms/step - loss: 1.0342 - accuracy: 0.6274 - val_loss: 1.0136 - val_accuracy: 0.6456 Epoch 17/50 26/26 [==============================] - 1s 19ms/step - loss: 0.9980 - accuracy: 0.6432 - val_loss: 0.9976 - val_accuracy: 0.6602 Epoch 18/50 26/26 [==============================] - 0s 18ms/step - loss: 0.9692 - accuracy: 0.6590 - val_loss: 0.9352 - val_accuracy: 0.6699 Epoch 19/50 26/26 [==============================] - 0s 18ms/step - loss: 0.9677 - accuracy: 0.6711 - val_loss: 0.9452 - val_accuracy: 0.6748 Epoch 20/50 26/26 [==============================] - 0s 19ms/step - loss: 0.9255 - accuracy: 0.6590 - val_loss: 0.9195 - val_accuracy: 0.6942 Epoch 21/50 26/26 [==============================] - 0s 19ms/step - loss: 0.9031 - accuracy: 0.6845 - val_loss: 0.9164 - val_accuracy: 0.6845 Epoch 22/50 26/26 [==============================] - 1s 24ms/step - loss: 0.8453 - accuracy: 0.6905 - val_loss: 0.8817 - val_accuracy: 0.6699 Epoch 23/50 26/26 [==============================] - 1s 29ms/step - loss: 0.8972 - accuracy: 0.6869 - val_loss: 0.8991 - val_accuracy: 0.6602 Epoch 24/50 26/26 [==============================] - 1s 32ms/step - loss: 0.8386 - accuracy: 0.7015 - val_loss: 0.8837 - val_accuracy: 0.6796 Epoch 25/50 26/26 [==============================] - 1s 27ms/step - loss: 0.8203 - accuracy: 0.7124 - val_loss: 0.8930 - val_accuracy: 0.6699 Epoch 26/50 26/26 [==============================] - 0s 18ms/step - loss: 0.7915 - accuracy: 0.7257 - val_loss: 0.8809 - val_accuracy: 0.6845 Epoch 27/50 26/26 [==============================] - 1s 25ms/step - loss: 0.7470 - accuracy: 0.7476 - val_loss: 0.8580 - val_accuracy: 0.6748 Epoch 28/50 26/26 [==============================] - 1s 31ms/step - loss: 0.7667 - accuracy: 0.7245 - val_loss: 0.8523 - val_accuracy: 0.6796 Epoch 29/50 26/26 [==============================] - 1s 31ms/step - loss: 0.7686 - accuracy: 0.7403 - val_loss: 0.8236 - val_accuracy: 0.6990 Epoch 30/50 26/26 [==============================] - 1s 25ms/step - loss: 0.7383 - accuracy: 0.7367 - val_loss: 0.8259 - val_accuracy: 0.6893 Epoch 31/50 26/26 [==============================] - 0s 18ms/step - loss: 0.6816 - accuracy: 0.7536 - val_loss: 0.8214 - val_accuracy: 0.6893 Epoch 32/50 26/26 [==============================] - 0s 18ms/step - loss: 0.6481 - accuracy: 0.7852 - val_loss: 0.8540 - val_accuracy: 0.7136 Epoch 33/50 26/26 [==============================] - 0s 18ms/step - loss: 0.6911 - accuracy: 0.7621 - val_loss: 0.8308 - val_accuracy: 0.6893 Epoch 34/50 26/26 [==============================] - 0s 19ms/step - loss: 0.6173 - accuracy: 0.7828 - val_loss: 0.7821 - val_accuracy: 0.7184 Epoch 35/50 26/26 [==============================] - 0s 18ms/step - loss: 0.6389 - accuracy: 0.7913 - val_loss: 0.7905 - val_accuracy: 0.7136 Epoch 36/50 26/26 [==============================] - 0s 19ms/step - loss: 0.6521 - accuracy: 0.7488 - val_loss: 0.7979 - val_accuracy: 0.6796 Epoch 37/50 26/26 [==============================] - 0s 19ms/step - loss: 0.5834 - accuracy: 0.7925 - val_loss: 0.7902 - val_accuracy: 0.6990 Epoch 38/50 26/26 [==============================] - 0s 19ms/step - loss: 0.5795 - accuracy: 0.7913 - val_loss: 0.8223 - val_accuracy: 0.6845 Epoch 39/50 26/26 [==============================] - 0s 18ms/step - loss: 0.5922 - accuracy: 0.8034 - val_loss: 0.7989 - val_accuracy: 0.7087 Epoch 40/50 26/26 [==============================] - 0s 18ms/step - loss: 0.5981 - accuracy: 0.8095 - val_loss: 0.7923 - val_accuracy: 0.6990 Epoch 41/50 26/26 [==============================] - 0s 17ms/step - loss: 0.6018 - accuracy: 0.7900 - val_loss: 0.7932 - val_accuracy: 0.7233 Epoch 42/50 26/26 [==============================] - 0s 18ms/step - loss: 0.5972 - accuracy: 0.7949 - val_loss: 0.8355 - val_accuracy: 0.6942 Epoch 43/50 26/26 [==============================] - 0s 18ms/step - loss: 0.5222 - accuracy: 0.8277 - val_loss: 0.7805 - val_accuracy: 0.7233 Epoch 44/50 26/26 [==============================] - 1s 25ms/step - loss: 0.5742 - accuracy: 0.8070 - val_loss: 0.8051 - val_accuracy: 0.7233 Epoch 45/50 26/26 [==============================] - 1s 37ms/step - loss: 0.5962 - accuracy: 0.8119 - val_loss: 0.7760 - val_accuracy: 0.7427 Epoch 46/50 26/26 [==============================] - 1s 37ms/step - loss: 0.5538 - accuracy: 0.8022 - val_loss: 0.8090 - val_accuracy: 0.7184 Epoch 47/50 26/26 [==============================] - 1s 23ms/step - loss: 0.4936 - accuracy: 0.8216 - val_loss: 0.7777 - val_accuracy: 0.7039 Epoch 48/50 26/26 [==============================] - 0s 18ms/step - loss: 0.5010 - accuracy: 0.8374 - val_loss: 0.7834 - val_accuracy: 0.7427 Epoch 49/50 26/26 [==============================] - 0s 18ms/step - loss: 0.4935 - accuracy: 0.8277 - val_loss: 0.8089 - val_accuracy: 0.7330 Epoch 50/50 26/26 [==============================] - 0s 18ms/step - loss: 0.5017 - accuracy: 0.8228 - val_loss: 0.7851 - val_accuracy: 0.7184
Out[88]:
<keras.src.callbacks.History at 0x794914d0c940>
In [89]:
def cnn_activations(input, layer, filters):
conv_layer = model.get_layer(layer) #put name of the actual convolutional layer, based on the error message
conv_model = keras.Model(inputs=model.inputs, outputs=conv_layer.output)
activations = conv_model.predict(input)
activations = np.squeeze(activations, axis=0)
activation_map = activations[:, :, filters - 1]
return activation_map
In [90]:
image = X_test[0]
plt.imshow(image, cmap='gray')
image = np.expand_dims(image, axis=0)
image = np.expand_dims(image, axis=-1)
print(image.shape)
(1, 50, 37, 1)
In [94]:
plt.imshow(cnn_activations(image, 'conv2d_2', 1), cmap='gray')
1/1 [==============================] - 0s 103ms/step
Out[94]:
<matplotlib.image.AxesImage at 0x794914da2980>
In [95]:
X_train_cnn = [cnn_activations(np.expand_dims(np.expand_dims(x, axis=0), axis=-1), 'conv2d_2', 1) for x in X_train]
X_test_cnn = [cnn_activations(np.expand_dims(np.expand_dims(x, axis=0), axis=-1), 'conv2d_2', 1) for x in X_test]
1/1 [==============================] - 0s 257ms/step 1/1 [==============================] - 0s 136ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 99ms/step 1/1 [==============================] - 0s 134ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 168ms/step 1/1 [==============================] - 0s 116ms/step 1/1 [==============================] - 0s 178ms/step 1/1 [==============================] - 0s 161ms/step 1/1 [==============================] - 0s 182ms/step 1/1 [==============================] - 0s 128ms/step 1/1 [==============================] - 0s 128ms/step 1/1 [==============================] - 0s 166ms/step 1/1 [==============================] - 0s 110ms/step 1/1 [==============================] - 0s 71ms/step 1/1 [==============================] - 0s 94ms/step 1/1 [==============================] - 0s 71ms/step 1/1 [==============================] - 0s 75ms/step 1/1 [==============================] - 0s 74ms/step 1/1 [==============================] - 0s 73ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 97ms/step 1/1 [==============================] - 0s 79ms/step 1/1 [==============================] - 0s 78ms/step 1/1 [==============================] - 0s 77ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 75ms/step 1/1 [==============================] - 0s 83ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 71ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 74ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 79ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 76ms/step 1/1 [==============================] - 0s 82ms/step 1/1 [==============================] - 0s 74ms/step 1/1 [==============================] - 0s 74ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 71ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 87ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 79ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 95ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 76ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 76ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 82ms/step 1/1 [==============================] - 0s 74ms/step 1/1 [==============================] - 0s 71ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 1s 636ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 75ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 94ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 73ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 76ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 78ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 73ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 71ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 81ms/step 1/1 [==============================] - 0s 82ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 81ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 89ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 73ms/step 1/1 [==============================] - 0s 82ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 74ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 78ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 92ms/step 1/1 [==============================] - 0s 80ms/step 1/1 [==============================] - 0s 71ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 84ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 81ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 71ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 88ms/step 1/1 [==============================] - 0s 244ms/step 1/1 [==============================] - 0s 100ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 77ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 73ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 1s 722ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 90ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 73ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 95ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 73ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 60ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 73ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 76ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 92ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 77ms/step 1/1 [==============================] - 0s 66ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 69ms/step 1/1 [==============================] - 0s 73ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 77ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 71ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 71ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 39ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 75ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 61ms/step 1/1 [==============================] - 0s 65ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 78ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 90ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 49ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 51ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 48ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 57ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 67ms/step 1/1 [==============================] - 0s 64ms/step 1/1 [==============================] - 0s 62ms/step 1/1 [==============================] - 0s 93ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 72ms/step 1/1 [==============================] - 0s 84ms/step 1/1 [==============================] - 0s 78ms/step 1/1 [==============================] - 0s 70ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 63ms/step 1/1 [==============================] - 0s 78ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 56ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 46ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 59ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 42ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 41ms/step 1/1 [==============================] - 0s 78ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 53ms/step 1/1 [==============================] - 0s 58ms/step 1/1 [==============================] - 0s 50ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 68ms/step 1/1 [==============================] - 0s 54ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 47ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 55ms/step 1/1 [==============================] - 0s 52ms/step 1/1 [==============================] - 0s 43ms/step 1/1 [==============================] - 0s 44ms/step 1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 0s 41ms/step
In [96]:
X_train_cnn = np.array(X_train_cnn)
X_test_cnn = np.array(X_test_cnn)
X_train_cnn = X_train_cnn.reshape((X_train.shape[0], 48*35))
X_test_cnn = X_test_cnn.reshape((X_test.shape[0], 48*35))
SVM
In [97]:
svm_cnn = SVC(kernel = 'rbf', decision_function_shape='ovo')
svm_cnn.fit(X_train_cnn, y_train)
cnn_predictions = svm_cnn.predict(X_test_cnn)
print(classification_report(y_test, cnn_predictions, target_names = target_names))
ConfusionMatrixDisplay.from_estimator(svm_cnn, X_test_cnn, y_test, display_labels=target_names, xticks_rotation="vertical")
plt.tight_layout()
plt.show()
precision recall f1-score support
Ariel Sharon 1.00 0.17 0.29 12
Colin Powell 0.87 0.71 0.78 55
Donald Rumsfeld 0.85 0.52 0.65 21
George W Bush 0.64 0.97 0.77 106
Gerhard Schroeder 1.00 0.37 0.54 19
Hugo Chavez 0.83 0.33 0.48 15
Tony Blair 0.65 0.50 0.57 30
accuracy 0.71 258
macro avg 0.83 0.51 0.58 258
weighted avg 0.76 0.71 0.68 258
KNN
In [98]:
knn_cnn = KNeighborsClassifier(n_neighbors = 5)
knn_cnn.fit(X_train_cnn, y_train)
cnn_predictions = knn_cnn.predict(X_test_cnn)
print(classification_report(y_test, cnn_predictions, target_names = target_names))
ConfusionMatrixDisplay.from_estimator(knn_cnn, X_test_cnn, y_test, display_labels=target_names, xticks_rotation="vertical")
plt.tight_layout()
plt.show()
precision recall f1-score support
Ariel Sharon 0.24 0.42 0.30 12
Colin Powell 0.69 0.64 0.66 55
Donald Rumsfeld 0.33 0.43 0.38 21
George W Bush 0.66 0.77 0.71 106
Gerhard Schroeder 0.25 0.16 0.19 19
Hugo Chavez 0.60 0.20 0.30 15
Tony Blair 0.35 0.20 0.26 30
accuracy 0.55 258
macro avg 0.45 0.40 0.40 258
weighted avg 0.55 0.55 0.54 258
Decision Tree
In [99]:
tree_cnn = DecisionTreeClassifier()
tree_cnn.fit(X_train_cnn, y_train)
cnn_predictions = tree_cnn.predict(X_test_cnn)
print(classification_report(y_test, cnn_predictions, target_names = target_names))
ConfusionMatrixDisplay.from_estimator(tree_cnn, X_test_cnn, y_test, display_labels=target_names, xticks_rotation="vertical")
plt.tight_layout()
plt.show()
precision recall f1-score support
Ariel Sharon 0.20 0.17 0.18 12
Colin Powell 0.56 0.51 0.53 55
Donald Rumsfeld 0.17 0.14 0.15 21
George W Bush 0.60 0.60 0.60 106
Gerhard Schroeder 0.24 0.42 0.31 19
Hugo Chavez 0.38 0.20 0.26 15
Tony Blair 0.38 0.40 0.39 30
accuracy 0.47 258
macro avg 0.36 0.35 0.35 258
weighted avg 0.47 0.47 0.46 258
