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?
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)