From abdul.sw84 at gmail.com  Thu Feb  7 14:39:12 2019
From: abdul.sw84 at gmail.com (Abdul Abdul)
Date: Thu, 7 Feb 2019 15:39:12 -0400
Subject: [Python Glasgow] Keras question
Message-ID: <CAA+hu4Wsc3rSKJ2+gyq1xxqQB2sA6_F=MBAFEsnDyMGxnm4O1w@mail.gmail.com>

Hello,

I hope you are doing well. I just have a question on Keras that I couldn't
find a clear answer to, so thought of asking you directly, and totally
apologize for my disturbance.

I have the following Keras code that tries to train a combination of an
image and a vector of real number features:

    from keras.layers import (Conv2D, Flatten, Lambda, Dense, concatenate,
                             Dropout, Input )
    from keras.models import Model
    import pandas as pd
    import numpy as np
    import cv2
    import os

    def label_img(img):
        word_label = img.split('.')[-3]
        if word_label == 'r':
            return 1
        elif word_label == 'i':
            return 0

    train_directory = '/train'
    images = []
    y = []

    dataset = pd.read_csv('features.csv')

    dataset = dataset[[ 'first_value',
                        'second_value']]

    features = dataset.iloc[:,0:2].values

    for root, dirs, files in os.walk(train_directory):
        for file in files:
            image = cv2.imread(root + '/' + file)
            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            image = cv2.resize(image,(512,512),interpolation=cv2.INTER_AREA)
            images.append(image)
            label = label_img(file)
            y.append(label)

    images = np.asarray(images)
    images = images.reshape((-1,512,512,1))

    image_input = Input(shape=(512,512,1))
    aux_input = Input(shape=(2,))

    input_layer = Conv2D(32,(5,5), activation='relu')(image_input)
    cov1 = Conv2D(24,(5,5), activation='relu', subsample=(2,2))(input_layer)
    cov2 = Conv2D(36,(5,5), activation='relu', subsample=(2,2))(cov1)
    cov3 = Conv2D(48,(5,5), activation='relu', subsample=(2,2))(cov2)
    cov4 = Conv2D(64,(5,5), activation='relu')(cov3)
    cov5 = Conv2D(64,(3,3), activation='relu')(cov4)
    dropout = Dropout(0.5)(cov5)
    flatten = Flatten()(dropout)

    # Here we add in the feature vectors
    merge = concatenate([flatten, aux_input])

    d1 = Dense(100, activation='elu')(merge)
    d2 = Dense(50, activation='elu')(d1)
    d3 = Dense(10, activation='elu')(d2)
    out = Dense(1)(d3)

    model = Model(inputs=[image_input, aux_input], outputs=[out])
    model.compile(loss='binary_crossentropy',
optimizer='adam',metrics=['acc'])
    model.fit([images, features], y, epochs=50)

However, I'm getting something like the following:

    Epoch 2/50
    5/5 [==============================] - 0s - loss: nan - acc: 0.0000e+00
    Epoch 3/50
    5/5 [==============================] - 0s - loss: nan - acc: 0.0000e+00
    Epoch 4/50
    5/5 [==============================] - 0s - loss: nan - acc: 0.0000e+00
    Epoch 5/50
    ...
    ...
    ...

How do you think I can solve this issue?

I would like to mention that my images are black background images with
foreground showing an edge of the original image. I'm not sure if that's an
issue at all, as there is not much data in the image?

Thanks a lot.
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