Keras input_shape for conv2d and manually loaded images
convolution, keras, neural-network, python, tensorflow
Solution
Set the `input_shape` to (286,384,1). Now the model expects an input with 4 dimensions. This means that you have to reshape your image with `.reshape(n_images, 286, 384, 1)`. Now you have added an extra dimension without changing the data and your model is ready to run. Basically, you need to reshape your data to (`n_images`, `x_shape`, `y_shape`, `channels`).
The cool thing is that you also can use an RGB-image as input. Just change `channels` to 3.
Check also this answer: Keras input explanation: input_shape, units, batch_size, dim, etc
Example
import numpy as np
from keras.models import Sequential
from keras.layers.convolutional import Convolution2D
from keras.layers.core import Flatten, Dense, Activation
from keras.utils import np_utils
#Create model
model = Sequential()
model.add(Convolution2D(32, kernel_size=(3, 3), activation='relu', input_shape=(286,384,1)))
model.add(Flatten())
model.add(Dense(2))
model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
#Create random data
n_images=100
data = np.random.randint(0,2,n_images*286*384)
labels = np.random.randint(0,2,n_images)
labels = np_utils.to_categorical(list(labels))
#add dimension to images
data = data.reshape(n_images,286,384,1)
#Fit model
model.fit(data, labels, verbose=1)
Problem
I am manually creating my dataset from a number of 384x286 b/w images. I load an image like this: ``` x = [] for f in files: img = Image.open(f) img.load() data = np.asarray(img, dtype="int32") x.append(data) x = np.array(x) ``` this results in x being an array (num_samples, 286, 384) ``` print(x.shape) => (100, 286, 384) ``` reading the keras documentation, and checking my backend, i should provide to the convolution step an input_shape composed by ( rows, cols, channels ) since i don't arbitrarily know the sample size, i would have expected to pass as an input size, something similar to ``` ( None, 286, 384, 1 ) ``` the model is built as follows: ``` model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) # other steps... ``` passing as input_shape (286, 384, 1) causes: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (85, 286, 384) passing as_input_shape (None, 286, 384, 1 ) causes: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=5 what am i doing wrong ? how do i have to reshape the input array?