Keras input shape error - passing the whole array not each line - arrays

Question

I am loading images from a csv file. The images are 300 x 300 pixels but flattened to 90000. I am getting an error for input shape. I am using tensorflow back end. I have attached an image of my csv file as well as an image of the error. It looks like its passing the whole list of arrays instead of passing each line.
"ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 arrays but instead got the following list of 380 arrays:[array([ 43., 45., 46., ..., 161., 152., 146.]), array([ 211., 222., 224., ..., 212., 213., 213.]), array([ 201., 201., "
csv file
error
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout
import csv
import cv2
import re
loaded_images_train = []
loaded_labels_train = []
loaded_images_test = []
loaded_labels_test = []
with open('images_train.csv') as f:
csvReader = csv.reader(f, lineterminator = '\n')
for row in csvReader:
row = np.asarray(row, dtype='float')
loaded_images_train.append(row)
with open('labels_train.csv') as f:
csvReader = csv.reader(f, lineterminator = '\n')
for row in csvReader:
row = str(row)
row = row.strip(',')
loaded_labels_train.append(row)
with open('images_test.csv') as f:
csvReader = csv.reader(f, lineterminator = '\n')
for row in csvReader:
row = np.asarray(row, dtype='float')
loaded_images_test.append(row)
with open('labels_test.csv') as f:
csvReader = csv.reader(f, lineterminator = '\n')
for row in csvReader:
row = str(row)
row = row.strip(',')
loaded_labels_test.append(row)
# load data
x_train = loaded_images_train
y_train = loaded_labels_train
print("Loaded Training Data")
x_test = loaded_images_test
y_test = loaded_labels_test
print("Loaded Testing Data")
model = Sequential()
model.add(Dense(64, input_shape=(90000,), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model.fit(x_train, y_train,
epochs=20,
batch_size=128)
#score = model.evaluate(x_test, y_test, batch_size=128)
print(score)

Answer 1

The way you are converting each line with asarray and then feeding keras with a list of arrays is not working.
I've tested your code with a sightly different approach and it did run flawlessly for me with the csv you provided in the comments (changing input_size to 400).
Read all lines from the file to loaded_images_train. It will be a list of lists:
input_size = 90000
with open('images_train.csv') as f:
csvReader = csv.reader(f, lineterminator = '\n')
for row in csvReader:
assert len(row) == input_size
loaded_images_train.append(row)
I've included the assertion following your feedback to my comment.
You can also assert len(row) == output_size for the labels.
On the other hand, if you are pretty sure about the sizes of the rows, you can substitute the loop by a simple:
loaded_images_train = list(csvReader)
Whichever you choose, do the same to test images.
Then do the conversion to numpy.ndarray when declaring x_train:
x_train = np.asarray(loaded_images_train, dtype=float) # you don't really need the quotes here
Finally, printing the shape of the loaded data can help you know that everything is ok. For example:
print("Loaded Training Data", x_train.shape)

Answer 2

The reason why you met the problem is the type of your dataset is list, but the acceptable type for Keras model is only numpy array.
You need to convert the lists to numpy array with np.asarray(loaded_images_train) and make sure the shape of the data is (n,90000).