In ImageDataGenerator of Keras the flow method has argument x which takes data with rank 4. Why?
ValueError: ('Input data in
NumpyArrayIterator
should have rank 4. You passed an array with shape', (3, 150, 150))
For the reference, my code is as per follow:
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
# dimensions of our images.
img_width, img_height = 150, 150
train_data_dir = 'C:/Users/imageNetToyDataset/train'
validation_data_dir = 'C:/Users/imageNetToyDataset/validation'
epochs = 5
nb_train_samples = 2000
nb_validation_samples = 50
batch_size = 16
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
import numpy as np
import cv2
import csv
import os
from keras.preprocessing.image import ImageDataGenerator, array_to_img,
img_to_array, load_img
from scipy.misc import imresize
import scipy
def predict_labels(model):
"""writes test image labels and predictions to csv"""
test_data_dir = "C:/Users/imageNetToyDataset/test"
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
test_data_dir,
target_size=(img_width, img_height),
batch_size=32,
shuffle=False,
class_mode="binary")
with open("prediction.csv", "w") as f:
p_writer = csv.writer(f, delimiter=',', lineterminator='\n')
for _, _, imgs in os.walk(test_data_dir):
print ("number of images: {}".format(len(imgs)))
for im in imgs:
print ("image:\n{}".format(im))
pic_id = im.split(".")[0]
imgPath = os.path.join(test_data_dir,im)
print (imgPath)
img = load_img(imgPath)
img = imresize(img, size=(img_width, img_height))
print ("img shape = {}".format(img.shape))
test_x = img_to_array(img).reshape(3, img_width, img_height)
print ("test_x shape = {}".format(test_x.shape))
test_generator = test_datagen.flow(test_x,
batch_size=1,
shuffle=False)
prediction = model.predict_generator(test_generator,1,epochs)
p_writer.writerow([pic_id, prediction])
prediction=predict_labels(model)
(code is run in jupyter notebook)
The forth dimension is the number of samples in a batch. Look at https://keras.io/preprocessing/image/ at the data_format explanation