Error when checking model input: expected lambda_input_4 to have 4 dimensions, but got array with shape (32, 1)

[waveroarer]

Hello, I got an error,

Code:

import csv
import cv2
import numpy as np
import sklearn
import os
from random import shuffle


lines = []
with open('./data/driving_log.csv') as csvfile:
    reader = csv.reader(csvfile)
    for line in reader:
        lines.append(line)
images = []
measurements = []
correction = 0.15

from sklearn.model_selection import train_test_split
train_samples, validation_samples = train_test_split(lines, test_size=0.2)

def generator(samples, batch_size=32):
    num_samples = len(lines)
    while 1: # Loop forever so the generator never terminates
        shuffle(samples)
        for offset in range(0, num_samples, batch_size):
            batch_samples = samples[offset:offset+batch_size]

            images = []
            angles = []
            for batch_sample in batch_samples:
                name = './IMG/'+batch_sample[0].split('\\')[-1]
                center_image = cv2.imread(name)
                center_angle = float(batch_sample[3])
                images.append(center_image)
                angles.append(center_angle)              

            # trim image to only see section with road
            X_train = np.array(images)
            y_train = np.array(angles)
            yield sklearn.utils.shuffle(X_train, y_train)

from keras.models import Sequential
from keras.layers import Cropping2D
from keras.layers import Flatten, Dense, Lambda
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import MaxPooling2D

train_generator = generator(train_samples, batch_size=32)
validation_generator = generator(validation_samples, batch_size=32)

model = Sequential()
model.add(Lambda(lambda x: x / 127.5 - 1, input_shape=(160,320,3),output_shape=(160,320,3)))
model.add(Cropping2D(cropping=((70,25),(0,0))))
model.add(Convolution2D(24,5,5,subsample=(2,2),activation="relu"))
model.add(Convolution2D(36,5,5,subsample=(2,2),activation="relu"))
model.add(Convolution2D(48,5,5,subsample=(2,2),activation="relu"))
model.add(Convolution2D(64,3,3,activation="relu"))
model.add(Convolution2D(64,3,3,activation="relu"))
model.add(Flatten())
model.add(Dense(100))
model.add(Dense(50))
model.add(Dense(10))
model.add(Dense(1))

model.compile(loss='mse',optimizer='adam')
model.fit_generator(train_generator, samples_per_epoch= len(train_samples), validation_data=validation_generator, nb_val_samples=len(validation_samples), nb_epoch=3)

model.save('model.h5')

Error:

_Epoch 1/3

ValueError Traceback (most recent call last)
in ()
109 # model.fit(X_train, y_train, validation_split=0.2,shuffle=True,nb_epoch=3)
110
--> 111 model.fit_generator(train_generator, samples_per_epoch= len(train_samples), validation_data=validation_generator, nb_val_samples=len(validation_samples), nb_epoch=3)
112
113 model.save('model.h5')

/home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/keras/models.py in fit_generator(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe, initial_epoch, **kwargs)
933 nb_worker=nb_worker,
934 pickle_safe=pickle_safe,
--> 935 initial_epoch=initial_epoch)
936
937 def evaluate_generator(self, generator, val_samples,

/home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/keras/engine/training.py in fit_generator(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe, initial_epoch)
1551 outs = self.train_on_batch(x, y,
1552 sample_weight=sample_weight,
-> 1553 class_weight=class_weight)
1554
1555 if not isinstance(outs, list):

/home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1308 sample_weight=sample_weight,
1309 class_weight=class_weight,
-> 1310 check_batch_axis=True)
1311 if self.uses_learning_phase and not isinstance(K.learning_phase, int):
1312 ins = x + y + sample_weights + [1.]

/home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1028 self.internal_input_shapes,
1029 check_batch_axis=False,
-> 1030 exception_prefix='model input')
1031 y = standardize_input_data(y, self.output_names,
1032 output_shapes,

/home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/keras/engine/training.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
110 ' to have ' + str(len(shapes[i])) +
111 ' dimensions, but got array with shape ' +
--> 112 str(array.shape))
113 for j, (dim, ref_dim) in enumerate(zip(array.shape, shapes[i])):
114 if not j and not check_batch_axis:

ValueError: Error when checking model input: expected lambda_input_4 to have 4 dimensions, but got array with shape (32, 1)_

[baumanab]

Thank you for letting us know. It isn't clear that this is a bug. Would you mind discussing with a mentor? If it is a bug, let us know, and we can re-open the issue.