CODEWITHSUNDEEP
pythonarraysimagenumpykeras
Deya
Deya

Reputation: 41

How to prepare multiple RGB images as numpy array for CNN

I want to use the code below with my own input images instead of the mnist images. However I am having a hard time inputting several color .jpg images into a numpy array similar to the X_train used in the code below. I have a folder called data with another folder called train that includes several images that I would like to use as my X_train. I can generate the labels for them and one-hot encode them. I just don't know how to make all my images go into a nice array like X_train. Help? I did look here, but I got a Value Error: setting an array element with a sequence when I just copied and pasted the summarized code. 

import numpy as np
np.random.seed(123)  # for reproducibility

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist

# 4. Load pre-shuffled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# 5. Preprocess input data
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

# 6. Preprocess class labels
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)

# 7. Define model architecture
model = Sequential()

model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(1,28,28)))
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

# 8. Compile model
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# 9. Fit model on training data
model.fit(X_train, Y_train, 
          batch_size=32, nb_epoch=10, verbose=1)

# 10. Evaluate model on test data
score = model.evaluate(X_test, Y_test, verbose=0)

Upvotes: 1

Views: 1869

Answers (1)

Wilmar van Ommeren
Wilmar van Ommeren

Reputation: 7699

I am assuming that you are using Theano and that your jpgs have 3 bands. In addition, your jpgs should have the same input shape as the input shape that you indicate in the first convolutional model (28x28 pixels). In that case you can reshape all your jpes with the following lines:

#create random data
no_of_jpgs = 10
jpgs = [np.random.randint(0,255,(28,28,3)) for i in range(no_of_jpgs)]
jpgs = np.array(jpgs)

#reshape data
jpgs.reshape(no_of_jpgs, jpgs.shape[1], jpgs.shape[2], 3)

now you have an array with: (features, n_bands, x, y)

In addition you should change your input_shape so that it supports 3 bands:

model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(3, 28,28)))

If you have jpgs with a different shape or more bands, just change the input_shape values in the first convolutional layer.

Upvotes: 1

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