What should be the input to Convolution neural network (CNN) using keras and tensorflow?

Question

I'm trying to create CNN model using keras ad tensorflow as backend. below is code for the same..

Cannot understand what input it is expecting...

import cv2,os
import glob
import numpy as np
from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Input, Convolution2D, MaxPooling2D, Dense, Dropout, Flatten

PATH = os.getcwd()
data_path = PATH + '/data1/cat/*.PNG'


files = glob.glob(data_path)
X_data = []
for myFile in files:
    image = cv2.imread (myFile)
    image_resize = cv2.resize(image,(128,128))
    X_data.append (image_resize)

image_data = np.array(X_data)
image_data = image_data.astype('float32')
image_data /= 255
print('X_data shape:', image_data.shape)



#Class ani labels
class_num = 2

total_Images = image_data.shape[0]
labels = np.ones((total_Images),dtype='int64')

labels[0:30] = 0
labels[31:] = 1


Y = to_categorical(labels,class_num)

#print(Y);



# Shuffle the dataset
x, y = shuffle(image_data, Y, random_state=2)
# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)



input_shape = image_data[0].shape

#print(input_shape)

model = Sequential()

conv1 = Convolution2D(32,(3,3),padding='same',activation='relu')(input_shape)
conv2 = Convolution2D(32,(3,3),padding='same',activation='relu')(conv1)
pool_1 = MaxPooling2D(pool_size=(2,2))(conv2)
drop1 = Dropout(0.5)(pool_1)

conv3 = Convolution2D(64,(3,3),padding='same',activation='relu')(drop1)
conv4 = Convolution2D(64,(3,3),padding='same',activation='relu')(conv3)

pool_2 = MaxPooling2D(pool_size=(2,2))(conv4)
drop2 = Dropout(0.5)(pool_2)


flat = Flatten()(drop2)
hidden = Dense(64,activation='relu')(flat)
drop3 = Dropout(0.5)(hidden)
out = Dense(class_num,activation='softmax')(drop3)

model.compile(loss = 'categorical_crossentropy', optimizer= 'adam', metrics=['accuracy'])


model.fit(X_train,y_train,batch_size=16,nb_epoch=20, verbose=1, validation_data=(X_test,y_test))

model.evaluate(X_test,y_test,verbose=1)

---

Error: ValueError: Layer conv2d_1 was called with an input that isn't a 
symbolic tensor. Received type: <class 'tuple'>. Full input: [(128, 128,3)]. 
All inputs to the layer should be tensors.

Answer 1

You are attempting to use the functional API and the sequential model all at once you need to first eliminate this line

model = Sequential()

Then, from the documentation of the functional API , we add an Input(channels,rows,columns) layer, and fill in the size values from your X_train matrix.

input_shape = Input()