application_vnd.jupyter.stderr_output_1.png
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda

from keras.models import Sequential, Model

from keras.regularizers import l2

from tensorflow.keras import Sequential

from tensorflow.keras import datasets, layers, models

from tensorflow.keras.layers import Flatten, Dense,BatchNormalization,Dropout,Input

cnn_model = Sequential()
cnn_model = models.Sequential()
cnn_model.add(layers.Conv2D(300, (5, 5), kernel_regularizer=l2(5e-05), padding='Same', activation='relu', input_shape=(150, 150, 3)))
cnn_model.add(BatchNormalization())
cnn_model.add(layers.MaxPooling2D(2, 2))
cnn_model.add(layers.Conv2D(200, (3, 3), kernel_regularizer=l2(5e-05), padding='same', activation='relu'))
cnn_model.add(BatchNormalization())
cnn_model.add(Dropout(0.2))
cnn_model.add(layers.MaxPooling2D(2, 2))
cnn_model.add(layers.Conv2D(100, (3, 3), kernel_regularizer=l2(5e-05), padding='same', activation='relu'))
cnn_model.add(BatchNormalization())
cnn_model.add(layers.MaxPooling2D(2, 2))
cnn_model.add(layers.Conv2D(64, (3, 3), kernel_regularizer=l2(5e-05), padding='same', activation='relu'))
cnn_model.add(BatchNormalization())
cnn_model.add(Dropout(0.2))
cnn_model.add(layers.MaxPooling2D(2, 2))