mrtushartiwari

data = [['TATA','BIRLA'],['JIO','TATA'],['AMBANI']]
pd.DataFrame(data)

"""
        0      1
0    TATA  BIRLA
1     JIO   TATA
2  AMBANI   None

mrtushartiwari

mrtushartiwari

KERAS_MODEL_NAME = "tf_effienet_Unet_brain_final"
effienet_Unet_model.save(KERAS_MODEL_NAME)
#Save this folder permanent storage for future use.

#reloading the model
tf.keras.backend.clear_session()
reloaded_model = build_effienet_unet(input_shape)

reloaded_model.load_weights("/content/tf_effienet_Unet_brain_final")

mrtushartiwari

from keras.losses import binary_crossentropy
import keras.backend as K
import tensorflow as tf

smooth=1e-6

def tversky(y_true, y_pred):
    y_true_pos = K.flatten(y_true)
    y_pred_pos = K.flatten(y_pred)
    true_pos = K.sum(y_true_pos * y_pred_pos)

mrtushartiwari

# https://github.com/Nupurgopali/Brain-tumor-classification-using-CNN/blob/master/convolutional_neural_network.py

simple_classifer = tf.keras.models.Sequential()

simple_classifer.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='relu', input_shape=[256, 256, 3]))
simple_classifer.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=1))

simple_classifer.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
simple_classifer.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=1))

mrtushartiwari

w = 256
h = 256
def image_augmentation(image_path, mask_path):
    """
    Takes the path of image and mask and apply any one of the image augmentation and save the image

    """
    I = cv2.imread(image_path)
    J = cv2.imread(image_path)
    oper = np.random.randint(low= 1, high=3)

mrtushartiwari

ax = images_df["is_tumor"].value_counts().plot(kind = "bar",title = "Is_tumor",color=['g','r'] )
for patch in ax.patches:
    ax.annotate(xy = (patch.get_x() + 0.25 ,patch.get_height()+0.5 ),s = str(patch.get_height()) )
    
    
print("Percentage of patients with no tumor", str( (images_df["is_tumor"].value_counts()[0]/ images_df["is_tumor"].value_counts().sum() ) *100) )
print("Percentage of patients with  tumor", str( (images_df["is_tumor"].value_counts()[1]/ images_df["is_tumor"].value_counts().sum() ) *100) )

mrtushartiwari

def istumor(mask_img):
    """
    Finding wheather the mask has tumor or not
    """
    mask_img = cv2.imread(mask_img)
    k = np.max(mask_img)
    return 1 if k > 0 else 0
    
    
images_df["is_tumor"] = images_df['MRI_mask'].apply(istumor)

mrtushartiwari

for i in stock_selected:
    fetch_data(symbol = i)
    time.sleep(10)
    print(i)
print(len(os.listdir("stock_data"))) # 156 stocks data downloaded

mrtushartiwari

f_and_o = pd.read_csv("FandO.csv") # File containing the F&O stocks.

stock_selected = []
for i in f_and_o['Symbol'].values:
    stock_selected.append(i)

# Removing the index future and option contracts
stock_selected.remove('NIFTY')
stock_selected.remove('BANKNIFTY')
stock_selected.remove('FINNIFTY')