CODEWITHSUNDEEP
pythonpython-3.xtensorflowdeep-learningartificial-intelligence
Sagi zawi
Sagi zawi

Reputation: 31

why tensorflow reshape array is out of range

I have array reshape and sizes issue

I haven't try anything due to the reason I am still new in this and I dont want to mess up things that are unreleated to the issue

import tensorflow as tf
import numpy as np


mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()

x_train = tf.keras.utils.normalize(x_train, axis=1)  # scales data between 0 and 1
x_test = tf.keras.utils.normalize(x_test, axis=1) 

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(32,)))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))

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

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

model.fit(x_train, y_train, epochs=3)

val_loss, val_acc = model.evaluate(x_test, y_test)
print(val_loss)
print(val_acc)

  File "t1.py", line 17, in <module>
    x_train = np.reshape(x_train, (x_train.shape[0], 1, x_train.shape[1]))
  File "<__array_function__ internals>", line 6, in reshape
  File "H:\Program Files\Python36\lib\site-packages\numpy\core\fromnumeric.py", line 301, in reshape
    return _wrapfunc(a, 'reshape', newshape, order=order)
  File "H:\Program Files\Python36\lib\site-packages\numpy\core\fromnumeric.py", line 61, in _wrapfunc
    return bound(*args, **kwds)
ValueError: cannot reshape array of size 47040000 into shape (60000,1,28)```

Upvotes: 1

Views: 52

Answers (1)

Sagi zawi
Sagi zawi

Reputation: 31

model.add(tf.keras.layers.Flatten(input_shape=(28,28)))

it is an 28x28 image not a 32 vector soo there we know it is not should be a vector of 32 by lefting an argument

Upvotes: 1

Related Questions