Creating a CNN Model

Question

I am trying to execute below code but getting an error as : NameError: name 'scipy' is not defined I have installed scipy and imported it as well, python version = 3.9 , tensorflow version = 2.15.0, its running fine upto model compilation but raising an error while training the model

from tensorflow.keras.preprocessing.image import ImageDataGenerator 
import scipy

# set random seed 
tf.random.set_seed(99)

# preprocess data (get all of the pixel values between 0 & 1 also called scaling or normalization) 
train_data_gen = ImageDataGenerator(rescale=1.0/255)
test_data_gen = ImageDataGenerator(rescale=1.0/255)

# Setup path for data directories 
train_data_path = 'pizza_steak/train/'
test_data_path = 'pizza_steak/test/'

# It creates data and labels automatically for us 
train_data = train_data_gen.flow_from_directory(
    directory = train_data_path,
    batch_size = 32, # batch_size=32 means that there are 32 training instances in each batch.
    target_size = (224,224), # # resize to this size
    class_mode = 'binary',
    seed = 42
)

test_data = test_data_gen.flow_from_directory(
    directory = test_data_path ,
    batch_size = 32,
    target_size = (224,224),
    class_mode = 'binary',
    seed = 42
)

# Buiding a CNN model 

model_1 = tf.keras.Sequential([
    tf.keras.layers.Conv2D(filters=10,
                           kernel_size = 3,
                           activation = 'relu',
                           input_shape = (224,224,3)), # input shape from train_generator as we are resizing to 224,224
    
    tf.keras.layers.Conv2D(10, 3, activation='relu'),
    tf.keras.layers.MaxPool2D(pool_size=2,
                              padding = 'valid'),

    tf.keras.layers.Conv2D(10, 3, activation='relu'),
    tf.keras.layers.Conv2D(10,3, activation='relu'),
    tf.keras.layers.MaxPool2D(2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(1, activation = 'sigmoid')

])

# compile our CNN 
model_1.compile(
    loss = "binary_crossentropy",
    optimizer = tf.keras.optimizers.Adam(),
    metrics = ["accuracy"]
)

# fit the model 
model_1.fit(train_data, 
            epochs=5,
            steps_per_epoch = len(train_data),
            validation_data = test_data,
            validation_steps = len(test_data))

# we dont have to pass X,y here as flow_from_directory does it for us automatically

Answer 1

Its working, after installation I had to restart my kernel.