CODEWITHSUNDEEP
pythonnumpytensorflowkerasdeep-learning
Vasudha Jain
Vasudha Jain

Reputation: 103

ValueError: Input 0 of layer sequential_2 is incompatible with the layer

I have the following code:

import tensorflow as tf
import keras
from keras.datasets import cifar10

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

import numpy as np

x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], x_train.shape[2], 3))
print(x_train.shape)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], x_test.shape[2], 3))
print(x_test.shape)

x_train = x_train.astype('float32')/255.0
x_test  = x_test.astype('float32')/255.0

from keras.utils import to_categorical
y_train = to_categorical(y_train, num_classes = 10)
y_test = to_categorical(y_test, num_classes = 10)


from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten

model = Sequential()
#Defining layers of the model
model.add(Dense(2056, activation='relu', input_shape = (3072,)))
model.add(Dense(10, activation='softmax')) 

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

history = model.fit(x_train, y_train, batch_size=1000, epochs=50)

And I am facing the following error:

ValueError: Input 0 of layer sequential_2 is incompatible with the layer: expected axis -1 of input shape to have value 3072 but received input with shape (1000, 32, 32, 3)

I want to keep the input_shape as 3072 only. How can I reshape my y_test to solve this?

Upvotes: 1

Views: 4973

Answers (1)

Prefect
Prefect

Reputation: 1777

You should Flatten your input data before passing them to Dense layer.

model = Sequential()
#Defining layers of the model
model.add(Flatten(input_shape=(32,32,3)) # 32*32*3 = 3072
model.add(Dense(2056, activation='relu'))
model.add(Dense(10, activation='softmax'))

This should fix the problem.

Upvotes: 2

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