Error when checking input: expected conv2d_1_input to have shape (64, 64, 3) but got array with shape (64, 64, 4)

Question

My model for classifying images is shown below.When using the predict function it is giving the below error:

ValueError: Error when checking input: expected conv2d_1_input to have shape (64, 64, 3) but got array with shape (64, 64, 4)

The model is as shown below:

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', 
metrics = ['accuracy'])

# Part 2 - Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
import pickle
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('Dataset/training_data',
target_size = (64, 64),batch_size = 32,class_mode = 'binary')
test_set = test_datagen.flow_from_directory('Dataset/test_data',
target_size = (64, 64),batch_size = 32,class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 350,epochs = 2,validation_data = test_set,validation_steps 
= 101)

The predict function is shown below: I have used requests package since I want to use a url of an image for prediction.

import requests
from io import BytesIO
from PIL import Image
import numpy as np
from keras.preprocessing import image
import cv2

 url='http://answers.opencv.org/upfiles/logo_2.png'
 response = requests.get(url)
 img = Image.open(BytesIO(response.content))
 #file = cv2.imread(img)
 img = img.resize((64,64))
 x = image.img_to_array(img)
 x = np.expand_dims(x, axis=0)
 result = classifier.predict(x)
 #training_set.class_indices
 if result[0][0] == 1:
      prediction = 'signature'
 else:
      prediction = 'nonsignature'



 print(prediction)

Is there an alternate way using only one package instead of PIL and keras

Thanks for help!!

sdcbr · Accepted Answer · 2019-01-29T13:10:05.527

The error message speaks for itself. Your network expects images with 3 color channels (RGB), but you are providing it images with 4 channels. You are working with a png image, so the image is probably in RGBA format and the fourth channel is a transparancy channel.

PIL images can be converted to RGB format as follows:

img = img.convert(mode='RGB')
x = image.img_to_array(img)

x.shape
> (64, 64, 3)

However, this conversion might not give the desired result (the background will be black for instance). You can refer to this question for other ways of converting to RGB.

You can also use the load_img function from the Keras preprocessing module:

import keras
img = keras.preprocessing.image.load_img(io.BytesIO(response.content))

This will load the image in RGB format. This function uses PIL under the hood and produces the same result as above.

Error when checking input: expected conv2d_1_input to have shape (64, 64, 3) but got array with shape (64, 64, 4)

1 Answers1