train.py

from google.colab import files

uploaded = files.upload()

for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(
      name=fn, length=len(uploaded[fn])))
  
# Then move kaggle.json into the folder where the API expects to find it.
!mkdir -p ~/.kaggle/ && mv kaggle.json ~/.kaggle/ && chmod 600 ~/.kaggle/kaggle.json
!kaggle datasets download -d navoneel/brain-mri-images-for-brain-tumor-detection
import tensorflow as tf
from zipfile import ZipFile
import os,glob
import cv2
from tqdm._tqdm_notebook import tqdm_notebook as tqdm
import numpy as np
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Convolution2D, Dropout, Dense,MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from zipfile import ZipFile
file_name = "/content/brain-mri-images-for-brain-tumor-detection.zip"
with ZipFile(file_name,'r') as zip:
  zip.extractall()
  print('Done')
  os.chdir('/content/yes')
X = []
y = []
for i in tqdm(os.listdir()):
      img = cv2.imread(i)   
      img = cv2.resize(img,(224,224))
      X.append(img)
      y.append((i[0:1]))
      print(i[0:1])
os.chdir('/content/no')
for i in tqdm(os.listdir()):
      img = cv2.imread(i)   
      img = cv2.resize(img,(224,224))
      X.append(img)
for i in range(1,99):
    y.append('N')
print(y)
%matplotlib inline
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
for i in range(4):
    plt.subplot(1, 4, i+1)
    plt.imshow(X[i], cmap="gray")
    plt.axis('off')
plt.show()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
print ("Shape of an image in X_train: ", X_train[0].shape)
print ("Shape of an image in X_test: ", X_test[0].shape)
le = preprocessing.LabelEncoder()
y_train = le.fit_transform(y_train)
y_test = le.fit_transform(y_test)
y_train = tf.keras.utils.to_categorical(y_train, num_classes=2)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=2)
y_train = np.array(y_train)
X_train = np.array(X_train)
y_test = np.array(y_test)
X_test = np.array(X_test) 
print("X_train Shape: ", X_train.shape) 
print("X_test Shape: ", X_test.shape)
print("y_train Shape: ", y_train.shape) 
print("y_test Shape: ", y_test.shape)
from keras.applications import vgg16


img_rows, img_cols = 224, 224 


vgg = vgg16.VGG16(weights = 'imagenet', 
                 include_top = False, 
                 input_shape = (img_rows, img_cols, 3))

# Here we freeze the last 4 layers 
# Layers are set to trainable as True by default
for layer in vgg.layers:
    layer.trainable = False
    
# Let's print our layers 
for (i,layer) in enumerate(vgg.layers):
    print(str(i) + " "+ layer.__class__.__name__, layer.trainable)
    def lw(bottom_model, num_classes):
    """creates the top or head of the model that will be 
    placed ontop of the bottom layers"""

    top_model = bottom_model.output
    top_model = GlobalAveragePooling2D()(top_model)
    top_model = Dense(1024,activation='relu')(top_model)
    top_model = Dense(1024,activation='relu')(top_model)
    top_model = Dense(512,activation='relu')(top_model)
    top_model = Dense(num_classes,activation='softmax')(top_model)
    return top_model 
    from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, GlobalAveragePooling2D
from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D

from keras.models import Model


num_classes = 2

FC_Head = lw(vgg, num_classes)

model = Model(inputs = vgg.input, outputs = FC_Head)

print(model.summary())
from tensorflow.keras.models import Model
model.compile(optimizer='adam', loss = 'categorical_crossentropy',metrics = ['accuracy'])
history = model.fit(X_train,y_train,
                    epochs=5, 
                    validation_data=(X_test,y_test),
                    verbose = 1,
                    initial_epoch=0)
                    import matplotlib.pyplot as plt
%matplotlib inline
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend(loc=0)
plt.figure()

plt.show()