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import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
%matplotlib inline              #to set the backend of matplotlib to the 'inline' backend

def load_dataset():
    train_dataset = h5py.File('catdataset/train_catvnoncat.h5', "r")

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index = 30      # change index value to visualize other data 
plt.imshow(train_set_x_orig[index])
print ("y = " + str(train_set_y[:, index]) + ", it's a '" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") +  "' picture.")

Crazz-Zaac

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

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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

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def HappyModel(input_shape):
    """
    Implementation of the HappyModel.
    
    Arguments:
    input_shape -- shape of the images of the dataset
        (height, width, channels) as a tuple.  
        Note that this does not include the 'batch' as a dimension.
        If you have a batch like 'X_train', 
        then you can provide the input_shape using

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##creating model
happyModel = HappyModel(X_train.shape[1:])

##compiling the model
happyModel.compile('adam', 'binary_crossentropy', metrics=['accuracy'])

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preds = happyModel.evaluate(X_test, Y_test, batch_size=32)
print()
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))

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img_path = 'images/pic5.jpeg' ##location to you image
img = image.load_img(img_path, target_size=(64, 64))
imshow(img)

x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

print(happyModel.predict(x))

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##prints the details of your layers in a table.
happyModel.summary()

##plots a graph of all the steps taken to build the model. 
##You can also save it in .png or .svg format
plot_model(happyModel, to_file='HappyModel.png')
SVG(model_to_dot(happyModel).create(prog='dot', format='svg'))

Crazz-Zaac

import argparse
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
import scipy.io
import scipy.misc
import numpy as np
import pandas as pd
import PIL
import tensorflow as tf