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— Jupyter Notebook. :
import tensorflow as tf
import numpy as np
from keras. preprocessing import image
from keras. preprocessing.image import ImageDataGenerator from matplotlib import pyplot as plt
from matplotlib import pyplot as plt
, Notebook, ! :
train_datagen = ImageDataGenerator(rescale = l*/255, shear_range = 0,2, zoom_range = 0.2, horizontal_flip = True)
flow_from_directory, , .
training_set = train_datagen.flow_from_directory('Location', target_size = (64,64), batch_size = 32, class_mode = 'binary')
Location . , ! cnn.add keras. , .
cnn = tf.keras. models.Sequential()
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size = 3, activation= 'relu ', input_shape = [64, 64, 3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size = 2, strides = 2))
cnn.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation = 'relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2) ))
cnn.add (tf.keras.layers.Flatten())
cnn.add(tf.keras.layers.Dense (units=128, activation = 'relu'))
cnn.add(tf.keras.layers.Dense(units = 1, activation = 'sigmoid'))
!
cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
cnn.fit(x = training_set, validationdata = test_set, epochs = 15)
, ! ( ) .
test_image = image.load_img('C:/Users/annah/Desktop/brain_tumor_dataset/training_set/Y2.jpg', target_size = (64, 64))
plt.imshow(test_image)
plt.title('Test Brain Image'), plt.xticks([]), plt.yticks([])
plt.show()
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = cnn.predict(test_image)
training_set.class_indices
if result[0][0] == 1:
prediction = 'brain tumor present'
else:
prediction = 'no brain tumor'
print(prediction)
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— , , . , . , "Machine Learning Deep Learning", , ML.
