Create a model (2) Sequential model methods

import os
import numpy as np
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
from keras import optimizers
import scipy
import pylab as pl
import matplotlib.cm as cm
%matplotlib inline

input_shape = (150, 150, 3)
img_width = 150
img_height = 150

nb_train_samples = 2000
nb_validation_samples = 1000
batch_size = 32
epochs = 10

train_data_dir = ‘D:/development/DeepLearningCV/datasets/catsvsdogs/train’
validation_data_dir = ‘D:/development/DeepLearningCV/datasets/catsvsdogs/validation’

1. used to rescale the pixel values from [0, 255] to [0, 1] interval
   datagen = ImageDataGenerator(rescale=1./255)

1. automagically retrieve images and their classes for train and validation sets
   train_generator = datagen.flow_from_directory(
   train_data_dir,
   target_size=(img_width, img_height),
   batch_size=16,
   class_mode=‘binary’)

validation_generator = datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode=‘binary’)

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation(‘relu’))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Conv2D(32, (3, 3)))
model.add(Activation(‘relu’))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Conv2D(64, (3, 3)))
model.add(Activation(‘relu’))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation(‘relu’))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation(‘sigmoid’))

print(model.summary())

model.compile(loss=‘binary_crossentropy’,
optimizer=‘rmsprop’,
metrics=[‘accuracy’])

---

Layer (type) Output Shape Param #
=============
conv2d_1 (Conv2D) (None, 148, 148, 32) 896
_________________________________________________________________
activation_1 (Activation) (None, 148, 148, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 74, 74, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 72, 72, 32) 9248
_________________________________________________________________
activation_2 (Activation) (None, 72, 72, 32) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 36, 36, 32) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 36, 36, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 34, 34, 64) 18496
_________________________________________________________________
activation_3 (Activation) (None, 34, 34, 64) 0
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 17, 17, 64) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 17, 17, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 18496) 0
_________________________________________________________________
dense_1 (Dense) (None, 64) 1183808
_________________________________________________________________
activation_4 (Activation) (None, 64) 0
_________________________________________________________________
dropout_4 (Dropout) (None, 64) 0
_________________________________________________________________
dense_2 (Dense) (None, 1) 65
_________________________________________________________________
activation_5 (Activation) (None, 1) 0
=============
Total params: 1,212,513
Trainable params: 1,212,513
Non-trainable params: 0
_________________________________________________________________
None