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Building The Happy House With The Keras Framework
Keras is a high-level neural networks API (programming fr...
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2018/07

Building The Happy House With The Keras Framework

Keras is a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK.

import numpy as np
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
import pydot
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from kt_utils import *

import keras.backend as K
K.set_image_data_format('channels_last')
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow

%matplotlib inline

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()

# Normalize image vectors
X_train = X_train_orig/255.
X_test = X_test_orig/255.

# Reshape
Y_train = Y_train_orig.T
Y_test = Y_test_orig.T

print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))

def HappyModel(input_shape):
    """
    Implementation of the HappyModel.
    
    Arguments:
    input_shape -- shape of the images of the dataset

    Returns:
    model -- a Model() instance in Keras
    """
    
    ### START CODE HERE ###
    # Feel free to use the suggested outline in the text above to get started, and run through the whole
    # exercise (including the later portions of this notebook) once. The come back also try out other
    # network architectures as well. 
    X_input = Input(input_shape)
    
    X = ZeroPadding2D((3,3))(X_input)
    X = Conv2D(32,(7,7),strides = (1,1),name = 'conv0')(X)
    X = BatchNormalization(axis = 3,name = 'bn0')(X)
    X = Activation('relu')(X)
    
    X = MaxPooling2D((2,2),name = 'max_pool')(X)
    
    X = Flatten()(X)
    X = Dense(1,activation = 'sigmoid',name='fc')(X)
    
    model = Model(inputs = X_input,outputs = X,name = 'HappyModel')
    
    ### END CODE HERE ###
    
    return model

#happyModel = HappyModel(X_train.shape[1:])
happyModel = HappyModel((64,64,3))

happyModel.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
happyModel.fit(x=X_train,y=Y_train,epochs=50,batch_size=50)

preds = happyModel.evaluate(x=X_test,y=Y_test)

We import Conv2D BatchNormalization Activation ZeroPadding2D MaxPooling2D Flatten Dense from keras to help us build the model.

And then we go through four steps to train the model:

  1. Create the model by calling the function above
  2. Compile the model by calling model.compile(optimizer = "...", loss = "...", metrics = ["accuracy"])
  3. Train the model on train data by calling model.fit(x = ..., y = ..., epochs = ..., batch_size = ...)
  4. Test the model on test data by calling model.evaluate(x = ..., y = ...)

we find that the optimizer adam effective, and the loss function is determined by the type of the function

For detailed API docs ,look up them in the https://keras.io/

 

Last modification:March 13th, 2019 at 07:06 pm
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