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import keras | |
from matplotlib.pyplot import title | |
from keras.models import Sequential,Input,Model | |
from keras.layers import Dense, Dropout, Flatten | |
from keras.layers import Conv2D, MaxPooling2D | |
from keras.layers.normalization import BatchNormalization | |
from keras.layers.advanced_activations import LeakyReLU | |
import tensorflow as tf | |
from tensorflow import keras | |
model = Sequential() | |
model.add(Conv2D(32, kernel_size=(3, 3),activation='linear',input_shape=(28,28,1),padding='same')) | |
model.add(LeakyReLU(alpha=0.1)) | |
model.add(MaxPooling2D((2, 2),padding='same')) | |
model.add(Conv2D(64, (3, 3), activation='linear',padding='same')) | |
model.add(LeakyReLU(alpha=0.1)) | |
model.add(MaxPooling2D(pool_size=(2, 2),padding='same')) | |
model.add(Conv2D(128, (3, 3), activation='linear',padding='same')) | |
model.add(LeakyReLU(alpha=0.1)) | |
model.add(MaxPooling2D(pool_size=(2, 2),padding='same')) | |
model.add(Flatten()) | |
model.add(Dense(128, activation='linear')) | |
model.add(LeakyReLU(alpha=0.1)) | |
model.add(Dense(num_classes, activation='softmax')) | |
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(),metrics=['accuracy']) |
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