<gh_stars>10-100
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.regularizers import l2

def create_model(input_shape):
    model = Sequential()

    model.add(Conv2D(64, (3,3), input_shape=input_shape))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(128, (3,3), kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(256, (3,3), kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(512, (3,3), kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Flatten())

    model.add(Dense(1024))
    model.add(Activation('elu'))
    model.add(Dropout(0.5))

    model.add(Dense(10))
    model.add(Activation('softmax'))

    # compile model
    model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics = ['accuracy'])

    return model

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.regularizers import l2

def create_model(input_shape):
    model = Sequential()

    model.add(Conv2D(64, (3,3), input_shape=input_shape))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(128, (3,3), kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(256, (3,3), kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(512, (3,3), kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
    model.add(Activation('elu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Flatten())

    model.add(Dense(1024))
    model.add(Activation('elu'))
    model.add(Dropout(0.5))

    model.add(Dense(10))
    model.add(Activation('softmax'))

    # compile model
    model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics = ['accuracy'])

    return model
# compile model