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Normalize image to 0 1 python?
Normalization can be performed to improve the contrast of an image or to standardize the pixel values for further processing. The offset from step 1 is still applied, but the scaling from step 1 is overwritten. Pass the parameters src, dst, alpha, beta, norm_type, dtype and mask. The offset from step 1 is still applied, but the scaling from step 1 is overwritten. We can use the normalize() function of OpenCV to normalize an image. I need to normalize it from input range to [0,255]. Trusted by business builders worldwide, the HubSpot Blogs are your number-on. If 1, independently normalize each sample, otherwise (if 0) normalize each feature. In this section, we will try to get a brief idea about how it works. There are several normalization techniques, but the most common ones include: Min-Max Scaling: Rescales data to a range of [0, 1] or [-1, 1]. # Load image in grayscaleimread('exampleIMREAD_GRAYSCALE) # Normalize the image. You can control this with either the vmin and vmax arguments or with the norm argument (if you want a non-linear scaling). One skill that is in high demand is Python programming. # Load image in grayscaleimread('exampleIMREAD_GRAYSCALE) # Normalize the image. edited Sep 15, 2016 at 10:27. In a normalized image: Mean = 0; Variance = 1. This is not guaranteed to always work in place; e if the data is a numpy array with an int dtype, a copy will be. In your case pixel values lie in the range [81628383]. As I want to categorize and compare photos in terms of their brightness, I want to normalize the V values (HSV) to be between 0 and 1 - with 1 being the brightest. n = n def __call__(self, tensor): return tensor/self. I use opencv with python. I want to normalize my image to a certain size. You can control this with either the vmin and vmax arguments or with the norm argument (if you want a non-linear scaling). The pixel values can range from 0 to 256. Image stretching and normalization#visualization module provides a framework for transforming values in images (and more generally any arrays), typically for the purpose of visualization. Mar 10, 2021 · OpenCV have a cv2. this is very well explained by @InnovArul above Understanding transform. Gamma and log contrast adjustment. ; label: A label for the plotted values. Feb 13, 2019 · I am looking for a faster approach to normalise image in Python. It is faster than loop approach when I use timeit, but inference pipeline got slower in 10 times (with for loop is about 50 FPS, with views about 5 FPS) Jan 29, 2021 · after using the function, this is the result of the image array: [[[12649761354 9504139193 12076008117] [12606061473 95 what operations do I need to perform/missing so that the output is in int? such as: Oct 26, 2015 · $\begingroup$ I'm not sure how the original transformation could fail to preserve the shape of the data. May 7, 2024 · This range is typically between 0 and 255 for images with 8-bit depth, where 0 represents black and 255 represents white. Gain a better understanding of how to handle inputs in your Python programs and best practices for using them effectively. Performs Logarithmic correction on the input image. Performs Logarithmic correction on the input image. We examined two normalization techniques — Residual Extraction and Min-Max Re-scaling. Primarily it does the job of. strange, but your approach with view’s is very slow. The Normalize() transform. Min-Max Re-scaling can be thought of as shifting and squeezing a distribution to fit on a scale between 0 and 1. If 1, independently normalize each sample, otherwise (if 0) normalize each feature. " -- that statement is false. For each value in an image, torchvisionNormalize(). 5 and then divide by 0. To make them [0 255] integer tensor before forwarding them to the network, I want to make them [0 1] float tensor again. As a quick example: import matplotlib data = [[0, 05, 0subplots() Linearly scales each image in image to have mean 0 and variance 1 Python v21 Overview;. Then, 2*normalized_input-1 will shift it between -1 and 1. This image demonstrates normal appearance of the ears in relation to the face. reduce_max(image) image = (image - min_) / (max_ - min_) + min_ image = tfper_image_standardization(image) However, I still wonder. sigma = int(5 * max / 300) For your case, you'll want to make sure all the floats round to the nearest integer, then you should be fine. This function transforms the input image pixelwise according to the equation O = gain*log(1 + I) after scaling each pixel to the range 0 to 1. Douwe Osinga and Jack Amadeo were working together at Sidewalk. The inputs to the ImageNormalize class are the data and the interval and stretch objects: ( png, svg, pdf) Jul 25, 2018 · this is very well explained by @InnovArul above Understanding transform. ToTensor ()" so the values were set to [0 1] float. When we examine the output of the above two lines we can see the maximum value of the image is 252 which has now mapped to 0. offset A tuple of mean values to be subtracted from the image. Step 3 - Convert to tensor. However, in most cases, you wouldn't need a 64-bit image. 0 are correct values. Hi, The issue is that numpy image is a byte/uint8 array and that is why there is conversion to ByteTensor in the source code I referenced. , 066666667, 1. $\endgroup$ – Approach 1: Using Min-Max Normalization. Nov 30, 2019 · As you work with two channels only, I assume that your domain might be fairly different from 3-channels natural images. Normalize is merely a shift-scale transform: output[channel] = (input[channel] - mean[channel]) / std[channel] The parameters names mean and std which seems rather misleading knowing. sum(class_input_data, axis = 0) isn't equal to 0, implying that I have done something wrong in my normalisation. We will use the default configuration and scale values to the range 0 and 1. Python is one of the most popular programming languages in the world. mean((1,2)) instead of just x In order to be able to broadcast you need to transpose the image first and then transpose back. Python is a popular programming language known for its simplicity and versatility. amin(img_array)) Will normalize your data between 0 and 1. Assuming activation function is ReLu. One is to divide each dimension by its standard deviation, once it has been zero-centered: (X /= np feature_range tuple (min, max), default=(0, 1) Desired range of transformed data. Step 5 - Normalize the image. A = [] May 14, 2019 · Normalization refers to normalizing the data dimensions so that they are of approximately the same scale. My question is, would normalizing images to [-1, 1] range be unfair to input pixels in negative range since through ReLu. normalize are not the desired mean and std, but rather the values to subtract and divide by, i, the estimated mean and std. normalize: (making your data range in [0, 1]) nor. The second argument is the destination image, creating an output image with our desired dimensions or size. It's equivalent to subtracting a constant and then dividing by a constant, which is what your proposal does, and which doesn't change the shape of the data. 5 yielding an image with mean zero and values in range [-1, 1] I tried the following already: I used this line of code to normalise the images I receive between a value of 0 and 1 : cv2. I am wondering if the sys. NORM_MINMAX) 1 The conversion is correct if the goal is to transform the minimum pixel value to -1, the maximum pixel value to 1, and linearly transform the pixels between the minimum and the maximumimshow assumes pixels below 0 are black, and above 1 are white. In some contexts, you need to normalize each image separately - for example adversarial datasets where each image has noise. Print the image data before and after Normalize. So in case of 16 bit image i would expect 0 and 65535. We examined two normalization techniques — Residual Extraction and Min-Max Re-scaling. array([2, 4, 6, 8]) >>> arr1 = values / values. The ML model is most likely a few conv 2d layers followed by a fully connected layers. admin edjoin Jan 18, 2021 · We have discussed the definition and general syntax of Cv2 Normalize. Step 3 - Convert to tensor. Print the image data before and after Normalize. sum(class_input_data, axis = 0) isn't equal to 0, implying that I have done something wrong in my normalisation. It automatically flattens the nested structure. The second part takes the "real" image (with stuff on it), and normalizes the RED, GREEN and BLUE channels, pixel by pixel, according to the background. You can do it per channel by specifying the axes as x. standardized_images_out = (rgb_images - mean) / std. In that case I would simply use 0. batch_norm_with_global_normalization; bidirectional_dynamic_rnn; Nov 12, 2020 · Conclusion. Need a Django & Python development company in France? Read reviews & compare projects by leading Python & Django development firms. Question: Do I still need a Min-Max Normalization, map the RGB value to 0-1? Jun 8, 2021 · If you want the range of values of every image to be between 0 and 255, you could loop over the images, calculate min and max of the original image and squeeze them, so the minimum is 0 and the maximum is 255min(img) max = np. reduce_min(image), tf. Normalization refers to scaling values of an array to the desired range. array([mean, std])) # Save mean and std as 2 elements numpy array after the "pixels" array (in the same file). sum(class_input_data, axis = 0) isn't equal to 0, implying that I have done something wrong in my normalisation. xmax: The minimum value in the dataset. Of course you'll first need to find the minimum and maximum. Answer by @Imanol is great, i just want to add some examples: Normalize the input either pixel wise or dataset wise. Residual Extraction can be thought of as shifting a distribution so that it’s mean is 0. Need a Django & Python development company in Zagreb? Read reviews & compare projects by leading Python & Django development firms. Normalization is done on the data to transform the data to appear on the same scale across all the records. It is faster than loop approach when I use timeit, but inference pipeline got slower in 10 times (with for loop is about 50 FPS, with views about 5 FPS) Jan 29, 2021 · after using the function, this is the result of the image array: [[[12649761354 9504139193 12076008117] [12606061473 95 what operations do I need to perform/missing so that the output is in int? such as: Oct 26, 2015 · $\begingroup$ I'm not sure how the original transformation could fail to preserve the shape of the data. lamborghini urus gearbox malfunction min() and the same for max, and the normalization is working just fine, giving me values between 0 and 1 Can you explain why you included it? – Feb 2, 2024 · It is used to get better contrast in images with poor contrast due to glare. Boost the performance further by re-using the average values to compute standard-deviation, according to its formula and hence inspired by this solution , like so -sqrt(((rgb_images - mean)**2). To normalize to the [0,1] range you should not use the mean and standard deviation, but the maximum and the minimum, as shown in Pitto's answer. The astropy. Looks good, but there are some things NumPy does that could make it nicer. The minimum and maximum values for each column are estimated from the dataset, and finally, the values in the dataset are normalized 2. ; color: The colour of the bars in the histogram. Normalize the exposure of an imagendarray} img: an array of image pixels with shape: 3. In PyTorch, you can normalize your images with torchvision, a utility that provides convenient preprocessing transformations. min() >>> arr1 array([ 1, 3]) The pixel values in a grayscale image are in [0, 255]. axis{0, 1}, default=1. In today’s world, remote work has become the new normal for many professionals. NORM_MINMAX) 1 The conversion is correct if the goal is to transform the minimum pixel value to -1, the maximum pixel value to 1, and linearly transform the pixels between the minimum and the maximumimshow assumes pixels below 0 are black, and above 1 are white. mean(axis=(0,1,2), keepdims=True) Note that we had to use keepdims herestd that works the same way, so we can do the whole normalization in. new builds in edinburgh We can see the command below. Answer by @Imanol is great, i just want to add some examples: Normalize the input either pixel wise or dataset wise. To normalize the values in a NumPy array to be between 0 and 1, you can use one of the following methods: Method 1: Use NumPy. No need to rewrite the normalization formula, thePyTorchlibrary takes care of everything! We simply use the Normalize ()function of the transforms module by indicating the mean and the standard deviation : norm = transforms4915, 04468), (02435, 0. transforms by the name of Normalize. Residual Extraction can be thought of as shifting a distribution so that it's mean is 0. A = [] May 14, 2019 · Normalization refers to normalizing the data dimensions so that they are of approximately the same scale. It is known for its simplicity and readability, making it an excellent choice for beginners who are eager to l. (This is equivalent to scaling the data down to 0,1) Therefore, it makes sense that the mean and std used in the 'transforms)' will be 03081, respectively. As you wrote it, I understand that you want to normalize the pixel values so that the norm of the vector obtained by stacking image columns is 1 If that is what you want, you can use meanStdDev ( documentation) and do the following (assuming your image is grayscale): cv::Scalar avg,sdv; cv::meanStdDev(image, avg, sdv); Is there a way to normalize a grayscale image so it could have a predefined mean value. However, I want to know can I do it with torchfunctional. This function transforms the input image pixelwise according to the equation O = gain*log(1 + I) after scaling each pixel to the range 0 to 1. You need to rearrange the channel dimension back, but. COLOR_BGR2YCrCb) # separate channelssplit(ycrcb) # get background which paper says (gaussian blur using standard deviation 5 pixel for 300x300 size image) # account for size of input vs 300. There's the "lazy man" approach: You can simply plug a nn. If you are a Python programmer, it is quite likely that you have experience in shell scripting. To make them [0 255] integer tensor before forwarding them to the network, I want to make them [0 1] float tensor again. Normalize which normalizes with mean and std. size multiplications is marginally faster thanmax()/255size divisions Since we are using basic numpy methods here, I think this is about as efficient a solution in numpy as can be. sum(axis=1, keepdims=1).
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Question: Do I still need a Min-Max Normalization, map the RGB value to 0-1? Vectorized is much faster than iterative. Find a company today! Development Most Popular E. edited Sep 15, 2016 at 10:27. ; color: The colour of the bars in the histogram. Would normalizing images into [0, 1] range instead be a better idea? How do you normalize all the image set? Should you regard on each image by itself or one normalization over all images? In our previous post A Tip A Day — Python Tip #7: OpenCV — CV2: imread () and resize (), we have explored a simple image and its pixel values. The ML model is most likely a few conv 2d layers followed by a fully connected layers. I searched through documentation and didn't find solution. Step 1 - Import library. transforms by the name of Normalize. Then, 2*normalized_input-1 will shift it between -1 and 1. There are two common ways of achieving this normalization. reshape(-1, 1) x_norm = prefit_transform(x) Jun 6, 2022 · Normalizing the images means transforming the images into such values that the mean and standard deviation of the image become 00 respectively. normalize([x_array])print(normalized_arr) Run the the complete example code to demonstrate how to normalize a NumPy array using the normalize () function: norm_numpy axis{0, 1}, default=1. The example first loads the dataset and converts the values for each column from string to floating point values. In this section, we will try to get a brief idea about how it works. Like 123 - 128 == 251, and then you divide it by 128array([28,25,24], dtype=np. Working with the code: Normalize an image in Python with OpenCV. Normalization in image processing is used to change the intensity level of pixels. kia new car delivery times Hence, all of them are already scaled in the same range [0-255]. It is faster than loop approach when I use timeit, but inference pipeline got slower in 10 times (with for loop is about 50 FPS, with views about 5 FPS). array ( [mean, std])) # Save mean and std as 2 elements numpy array after the "pixels" array (in the same file). reduce_max(image) image = (image - min_) / (max_ - min_) + min_ image = tfper_image_standardization(image) However, I still wonder. This transformation is. from sklearn import preprocessing as pre x = x. We bring the image in a range of intensity values, which makes the image less stressful and more normal to our senses. copy bool, default=True. My guess is that removing mean and dividing by std ( [-1,1]) will converge more quickly compared to a [0,1] normalization. In your example you subtract 0. You will have to write a custom transform. As you wrote it, I understand that you want to normalize the pixel values so that the norm of the vector obtained by stacking image columns is 1 If that is what you want, you can use meanStdDev ( documentation) and do the following (assuming your image is grayscale): cv::Scalar avg,sdv; cv::meanStdDev(image, avg, sdv); Is there a way to normalize a grayscale image so it could have a predefined mean value. No need to rewrite the normalization formula, thePyTorchlibrary takes care of everything! We simply use the Normalize ()function of the transforms module by indicating the mean and the standard deviation : norm = transforms4915, 04468), (02435, 0. The pixel values can range from 0 to 256. www fedex login It is faster than loop approach when I use timeit, but inference pipeline got slower in 10 times (with for loop is about 50 FPS, with views about 5 FPS) Jan 29, 2021 · after using the function, this is the result of the image array: [[[12649761354 9504139193 12076008117] [12606061473 95 what operations do I need to perform/missing so that the output is in int? such as: Oct 26, 2015 · $\begingroup$ I'm not sure how the original transformation could fail to preserve the shape of the data. Assuming your image img_array is an np. Nov 20, 2019 · So I have been trying to find a way to normalize some PIL image pixel values between -1 and 1. We will perform the following steps while normalizing images in PyTorch: Load and visualize image and plot pixel values. You can do it per channel by specifying the axes as x. Python is a versatile and powerful p. mean(axis=(0, 1, 2)) # Take the mean over the N,H,W axes means. datasets import mnist. Hence, all of them are already scaled in the same range [0-255]. astype('uint8') This first scales the vector to the [0, 1] range, multiplies it by 255 and then converts it to uint8, which is a common format for images (opencv uses it, for example) In general you can use: new_arr = ((arr - arrmax() - arrastype('uint8') edited Jun 20. min(x) – Minimum value in the dataset. CV_32F) Display the normalized output image. normalize(img, None, 0, 1NORM_MINMAX, dtype=cv2. We can use the normalize() function of OpenCV to normalize an image. So I am stuck on how to do it. white crochet vest We will use the default configuration and scale values to the range 0 and 1. WebsiteSetup Editorial Python 3 is a truly versatile programming language, loved both by web developers, data scientists, and software engineers. open(datafile) means = x. So I am stuck on how to do it. 0, then your range will be approx [06]. The data to normalize, element by elementsparse matrices should be in CSR format to avoid an un-necessary copy. However, I want to know can I do it with torchfunctional. feature_range tuple (min, max), default=(0, 1) Desired range of transformed data. You do: v = (origv - min)/(max - min) * 255 What this does is first map the values to [0,1] and then stretch them back to [0,255]. (x_train, y_train), (x_test, y_test) = mnist. sum(axis=0, keepdims=1) To normalize a vector within a specific range in Python using NumPy, you can follow a two-step process: Normalize the vector to a 0 to 1 range. Set to True to clip transformed values of held-out data to provided feature range. OS/HARDWARE: LINUX/P40 GPU with 8GB RAM.
; color: The colour of the bars in the histogram. visualization module provides an ImageNormalize class that wraps the interval (see Intervals and Normalization) and stretch (see Stretching) objects into an object Matplotlib understands. normalize() function on the input image img. The second step of method 2 scales the array so that the sum becomes 1. This tutorial explains how to normalize values in a NumPy array to be between 0 and 1, including several examples. The result of the following code gives me a black image. shape # => will evaluate to (C,) Then we can subtract the means from the whole dataset like so: centered = x - x. marcello bravo On the other hand, with the c, d as 1st and 99th percentile with no bounding, the mid-part of the histogram is centered to [0,1] and the lower and the upper 1 % of values are extended beyond this. Thank you. You can control this with either the vmin and vmax arguments or with the norm argument (if you want a non-linear scaling). It is faster than loop approach when I use timeit, but inference pipeline got slower in 10 times (with for loop is about 50 FPS, with views about 5 FPS) after using the function, this is the result of the image array: [[[12649761354 9504139193 12076008117] [12606061473 95 what operations do I need to perform/missing so that the output is in int? such as: $\begingroup$ I'm not sure how the original transformation could fail to preserve the shape of the data. shape # => will evaluate to (C,) Then we can subtract the means from the whole dataset like so: centered = x - x. normalize([x_array])print(normalized_arr) Run the the complete example code to demonstrate how to normalize a NumPy array using the normalize () function: norm_numpy axis{0, 1}, default=1. latchbio integer values 0 and 1 would be black and near-black. max(img) images[nr] = (img - min) / (max - min) * 255. clip bool, default=False. Python programming has gained immense popularity in recent years due to its simplicity and versatility. allblackx I tested something very simple on python. All values in-between get scaled to be within 0–1 range based on the original value relative to minimum and maximum values of the feature. Sep 5, 2020 · This function automatically scales the input data to the range of [0,1]. The function takes an array of data and calculates the norm.
Step 1 - Import library. According to this table, the float types correspond to the 32F depth. My guess is that removing mean and dividing by std ( [-1,1]) will converge more quickly compared to a [0,1] normalization. 5 yielding an image with mean zero and values in range [-1, 1] I tried the following already: I used this line of code to normalise the images I receive between a value of 0 and 1 : cv2. normalize([x_array])print(normalized_arr) Run the the complete example code to demonstrate how to normalize a NumPy array using the normalize () function: norm_numpy Feb 15, 2023 · The range in 0-1 scaling is known as Normalization. How would I normalize my data between -1 and 1? I have both negative and positive values in my data matrix $\begingroup$ Here is a Python gist of the Javascript command convertRange shared by Giuseppe Canale. When you scale this image by 255. In your case pixel values lie in the range [81628383]. To normalize the values in a NumPy array to be between 0 and 1, you can use one of the following methods: Method 1: Use NumPy. As the topic says, we will look into some of the cool feature provided by Python. normalize() function on the input image img. I need to normalize it from input range to [0,255]. To normalize the values in a NumPy array to be between 0 and 1, you can use one of the following methods: Method 1: Use NumPy. 1 Converting ndarray to grayscale. normalize … I don’t want to change images that are in the folder, because I want to visualize predicted images and I can’t see the original images with this way. NORM_MINMAX) 1 The conversion is correct if the goal is to transform the minimum pixel value to -1, the maximum pixel value to 1, and linearly transform the pixels between the minimum and the maximumimshow assumes pixels below 0 are black, and above 1 are white. reshape(-1, 1) x_norm = prefit_transform(x) Jun 6, 2022 · Normalizing the images means transforming the images into such values that the mean and standard deviation of the image become 00 respectively. (This is equivalent to normalizing zero mean and unit standard deviation. ) Hello @ptrblck!. Find a company today! Development Most Popular E. It’s these heat sensitive organs that allow pythons to identi. Otherwise it's just 0 or 1. Expert Advice On Improving Your Home Videos Latest View All. Looks good, but there are some things NumPy does that could make it nicer. craigslist food trailer for sale by owner class_input_data = class_input_data - column_mean. Explore 3D images (of cells) skimageadjust_log(image, gain=1, inv=False) [source] #. Working with the code: Normalize an image in Python with OpenCV. Both methods modify values into an array whose sum is 1, but they do it differently 1st method : scaling only. Suppose, we have an array = [1,2,3] and to normalize it in range [0,1] means that it will convert array [1,2,3] to [0, 0. 5 as mean and std to normalize the images in range (-1,1) but this will only work if our image data is already in (0,1) form and when i tried out normalizing my data (using mean and std as 0. zi = (xi – min (x)) / (max (x) – min (x)) where, xi – Value of the current iteration in your dataset. The ML model is most likely a few conv 2d layers followed by a fully connected layers. Hi, in the below code, I normalized the images with a formula. In min-max normalization, for every feature, its minimum value gets transformed into 0 and its maximum value gets transformed into 1. You may show the normalized image after converting the range to [0, 1]: cv2 pixnew[x,y] = (r,g,b) The first part of the code determines the maximum intensity of the RED, GREEN and BLUE channels, pixel by pixel, of the background image, but needs only be done once. Once you convert the video, you can use the images as normal JPG. To normalize a value, subtract. In today’s world, remote work has become the new normal for many professionals. In a normalized image: Mean = 0; Variance = 1. sum(axis=0, keepdims=1) says that alpha is the lower limit and beta upper limit. save (f, pixels) # Save the normalized imagesave (f, np. In OpenCV Python, the normalize() function from the cv2 module is used to normalize images The function returns two tuples: one for the training inputs and outputs and one for the test inputs and outputs 2 from keras. We have discussed the definition and general syntax of Cv2 Normalize. With the help of this, we can remove noise from an image. Hadley Wickham is the most important developer for the programming language R. Wes McKinney is amo. Gross domestic product, perhaps the most commonly used statistic in the w. gisselle lynette only fans Hence, all of them are already scaled in the same range [0-255]. Jun 19, 2020 · Most probably your images use some non-standard encoding scheme. Normalize which normalizes with mean and std. amax(img_array) - np. Normally, the pixel values (for a single channel) are bounded to [0255]. Compose([ transforms. In this article, we will introduce you to a fantastic opportunity to. n = n def __call__(self, tensor): return tensor/self. Now as you probably noticed passing 05 in Normalize would yield vales in range: Min of input image = 0 -> 0-05 -> gets divided by 0 Max of input image = 255 -> toTensor -> 1 -> (1 - 05 -> 1. astype('uint8') This first scales the vector to the [0, 1] range, multiplies it by 255 and then converts it to uint8, which is a common format for images (opencv uses it, for example) In general you can use: new_arr = ((arr - arrmax() - arrastype('uint8') edited Jun 20. The following normalizes each image according to its own min and max, assuming the inputs have typical size Batch x YDim x XDim x Channels: The model usage is simple: input = tfInput(shape=datasetshape) norm = tflayersNormalization() norm. The function takes an array of data and calculates the norm. May 4, 2019 · Assuming your image img_array is an np. Residual Extraction can be thought of as shifting a distribution so that it's mean is 0. Python is a powerful and versatile programming language that has gained immense popularity in recent years. In some contexts, you need to normalize each image separately - for example adversarial datasets where each image has noise. The first step of method 1 scales the array so that the minimum value becomes 1. Due to subtraction of mean, the values are however spread around 0, even going to negative. Step 5 - Normalize the image. In OpenCV Python, the normalize() function from the cv2 module is used to normalize images Jul 5, 2019 · The function returns two tuples: one for the training inputs and outputs and one for the test inputs and outputs 2 from keras.