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ValueError: Error when checking input: expected dense_18_input to have shape (784,) but got array with shape (1,)

Scikit learn (Python 3.5): Do I need to import a library to make this work?ValueError: Error when checking input: expected time_distributed_46_input to have 5 dimensions, but got array with shape (200, 200, 3)ValueError: Error when checking input: expected lstm_1_input to have 3 dimensions, but got array with shape (393613, 50)ValueError: Error when checking input: expected lstm_1_input to have 3 dimensions, but got array with shape (10, 1)Tensorflow keras with tf dataset inputValueError: Error when checking target: expected output to have shape (1,) but got array with shape (2,)LSTM Nerual Network Input/Output dimensions errorKeras batch_size problem when using model.fit in custom layersNeural Network classificationValueError: Error when checking input: expected conv1d_81_input to have shape (177, 100) but got array with shape (1, 177)

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0

I'm testing out tf.keras with tf.data, so I can do minibatch optimization. I'm using the MNIST dataset, and I'm running the code in Google Colab. However, when I try to train the network, I always get this error:
ValueError: Error when checking input: expected dense_18_input to have shape (784,) but got array with shape (1,). Here is my code:

import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import pandas as pd

!git clone https://github.com/DanorRon/my_repo
%cd my_repo
!ls

batch_size = 100
epochs = 10
alpha = 0.01
lambda_ = 0.01
h1 = 50

train = pd.read_csv('/content/sample_data/my_repo/mnist_train.csv.zip')
test = pd.read_csv('/content/sample_data/my_repo/mnist_test.csv.zip')

x_train = train.loc[:, '1x1':'28x28']
y_train = train.loc[:, 'label']

x_test = test.loc[:, '1x1':'28x28']
y_test = test.loc[:, 'label']

Train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
Train.batch(batch_size).repeat(10).shuffle(1000)

model = tf.keras.Sequential()
model.add(layers.Dense(784, input_shape=(784,)))
model.add(layers.Dense(h1, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
model.add(layers.Dense(10, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2(0.01)))

model.compile(optimizer=tf.train.AdamOptimizer(alpha),
 loss = 'categorical_crossentropy',
 metrics = ['accuracy'])

model.fit(Train, epochs=epochs, steps_per_epoch=600)

I don't know what the problem is. I think my dimensions are correct, and I can't see any other problem. How do I fix this problem?

Edit: I’ve looked more/tested things to find the answer, but I can’t find anything that works. I have no idea at all what the problem could be.

python tensorflow machine-learning google-colaboratory tf.keras

share|improve this question

edited Mar 23 at 15:16

Ronan Venkat

asked Mar 22 at 1:37

Ronan VenkatRonan Venkat

4415

add a comment | 

0

I'm testing out tf.keras with tf.data, so I can do minibatch optimization. I'm using the MNIST dataset, and I'm running the code in Google Colab. However, when I try to train the network, I always get this error:
ValueError: Error when checking input: expected dense_18_input to have shape (784,) but got array with shape (1,). Here is my code:

import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import pandas as pd

!git clone https://github.com/DanorRon/my_repo
%cd my_repo
!ls

batch_size = 100
epochs = 10
alpha = 0.01
lambda_ = 0.01
h1 = 50

train = pd.read_csv('/content/sample_data/my_repo/mnist_train.csv.zip')
test = pd.read_csv('/content/sample_data/my_repo/mnist_test.csv.zip')

x_train = train.loc[:, '1x1':'28x28']
y_train = train.loc[:, 'label']

x_test = test.loc[:, '1x1':'28x28']
y_test = test.loc[:, 'label']

Train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
Train.batch(batch_size).repeat(10).shuffle(1000)

model = tf.keras.Sequential()
model.add(layers.Dense(784, input_shape=(784,)))
model.add(layers.Dense(h1, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
model.add(layers.Dense(10, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2(0.01)))

model.compile(optimizer=tf.train.AdamOptimizer(alpha),
 loss = 'categorical_crossentropy',
 metrics = ['accuracy'])

model.fit(Train, epochs=epochs, steps_per_epoch=600)

I don't know what the problem is. I think my dimensions are correct, and I can't see any other problem. How do I fix this problem?

Edit: I’ve looked more/tested things to find the answer, but I can’t find anything that works. I have no idea at all what the problem could be.

python tensorflow machine-learning google-colaboratory tf.keras

share|improve this question

edited Mar 23 at 15:16

Ronan Venkat

asked Mar 22 at 1:37

Ronan VenkatRonan Venkat

4415

add a comment | 

I'm testing out tf.keras with tf.data, so I can do minibatch optimization. I'm using the MNIST dataset, and I'm running the code in Google Colab. However, when I try to train the network, I always get this error:
ValueError: Error when checking input: expected dense_18_input to have shape (784,) but got array with shape (1,). Here is my code:

import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import pandas as pd

!git clone https://github.com/DanorRon/my_repo
%cd my_repo
!ls

batch_size = 100
epochs = 10
alpha = 0.01
lambda_ = 0.01
h1 = 50

train = pd.read_csv('/content/sample_data/my_repo/mnist_train.csv.zip')
test = pd.read_csv('/content/sample_data/my_repo/mnist_test.csv.zip')

x_train = train.loc[:, '1x1':'28x28']
y_train = train.loc[:, 'label']

x_test = test.loc[:, '1x1':'28x28']
y_test = test.loc[:, 'label']

Train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
Train.batch(batch_size).repeat(10).shuffle(1000)

model = tf.keras.Sequential()
model.add(layers.Dense(784, input_shape=(784,)))
model.add(layers.Dense(h1, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
model.add(layers.Dense(10, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2(0.01)))

model.compile(optimizer=tf.train.AdamOptimizer(alpha),
 loss = 'categorical_crossentropy',
 metrics = ['accuracy'])

model.fit(Train, epochs=epochs, steps_per_epoch=600)

I don't know what the problem is. I think my dimensions are correct, and I can't see any other problem. How do I fix this problem?

Edit: I’ve looked more/tested things to find the answer, but I can’t find anything that works. I have no idea at all what the problem could be.

python tensorflow machine-learning google-colaboratory tf.keras

share|improve this question

edited Mar 23 at 15:16

Ronan Venkat

asked Mar 22 at 1:37

Ronan VenkatRonan Venkat

4415

import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import pandas as pd

!git clone https://github.com/DanorRon/my_repo
%cd my_repo
!ls

batch_size = 100
epochs = 10
alpha = 0.01
lambda_ = 0.01
h1 = 50

train = pd.read_csv('/content/sample_data/my_repo/mnist_train.csv.zip')
test = pd.read_csv('/content/sample_data/my_repo/mnist_test.csv.zip')

x_train = train.loc[:, '1x1':'28x28']
y_train = train.loc[:, 'label']

x_test = test.loc[:, '1x1':'28x28']
y_test = test.loc[:, 'label']

Train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
Train.batch(batch_size).repeat(10).shuffle(1000)

model = tf.keras.Sequential()
model.add(layers.Dense(784, input_shape=(784,)))
model.add(layers.Dense(h1, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
model.add(layers.Dense(10, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2(0.01)))

model.compile(optimizer=tf.train.AdamOptimizer(alpha),
 loss = 'categorical_crossentropy',
 metrics = ['accuracy'])

model.fit(Train, epochs=epochs, steps_per_epoch=600)

import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import pandas as pd

!git clone https://github.com/DanorRon/my_repo
%cd my_repo
!ls

batch_size = 100
epochs = 10
alpha = 0.01
lambda_ = 0.01
h1 = 50

train = pd.read_csv('/content/sample_data/my_repo/mnist_train.csv.zip')
test = pd.read_csv('/content/sample_data/my_repo/mnist_test.csv.zip')

x_train = train.loc[:, '1x1':'28x28']
y_train = train.loc[:, 'label']

x_test = test.loc[:, '1x1':'28x28']
y_test = test.loc[:, 'label']

Train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
Train.batch(batch_size).repeat(10).shuffle(1000)

model = tf.keras.Sequential()
model.add(layers.Dense(784, input_shape=(784,)))
model.add(layers.Dense(h1, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
model.add(layers.Dense(10, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2(0.01)))

model.compile(optimizer=tf.train.AdamOptimizer(alpha),
 loss = 'categorical_crossentropy',
 metrics = ['accuracy'])

model.fit(Train, epochs=epochs, steps_per_epoch=600)

import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import pandas as pd

!git clone https://github.com/DanorRon/my_repo
%cd my_repo
!ls

batch_size = 100
epochs = 10
alpha = 0.01
lambda_ = 0.01
h1 = 50

train = pd.read_csv('/content/sample_data/my_repo/mnist_train.csv.zip')
test = pd.read_csv('/content/sample_data/my_repo/mnist_test.csv.zip')

x_train = train.loc[:, '1x1':'28x28']
y_train = train.loc[:, 'label']

x_test = test.loc[:, '1x1':'28x28']
y_test = test.loc[:, 'label']

Train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
Train.batch(batch_size).repeat(10).shuffle(1000)

model = tf.keras.Sequential()
model.add(layers.Dense(784, input_shape=(784,)))
model.add(layers.Dense(h1, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
model.add(layers.Dense(10, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2(0.01)))

model.compile(optimizer=tf.train.AdamOptimizer(alpha),
 loss = 'categorical_crossentropy',
 metrics = ['accuracy'])

model.fit(Train, epochs=epochs, steps_per_epoch=600)

share|improve this question

edited Mar 23 at 15:16

Ronan Venkat

asked Mar 22 at 1:37

Ronan VenkatRonan Venkat

4415

share|improve this question

edited Mar 23 at 15:16

Ronan Venkat

asked Mar 22 at 1:37

Ronan VenkatRonan Venkat

4415

asked Mar 22 at 1:37

Ronan VenkatRonan Venkat

4415

asked Mar 22 at 1:37

Ronan VenkatRonan Venkat

4415

1 Answer
1

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oldest

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0

I guess maybe you should split x_train and y_train from Train, and rewrite model.fit as model.fit(x_train, y_train, epochs=epochs, steps_per_epoch=600).

share|improve this answer

answered Mar 22 at 2:22

daviddavid

597

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Is there a way to fix the problem without splitting the dataset?
  
  – Ronan Venkat
  Mar 22 at 13:13
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Also, how do I break the dataset apart? I can’t find anything on the docs.
  
  – Ronan Venkat
  Mar 22 at 16:40
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() Sorry, I haven't used the dataset in csv form. Generally, I use the api of keras. It has the same api with tensorflow.keras.
  
  – david
  Mar 25 at 2:24
  
  

  
  
  
  

add a comment | 

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0

I guess maybe you should split x_train and y_train from Train, and rewrite model.fit as model.fit(x_train, y_train, epochs=epochs, steps_per_epoch=600).

share|improve this answer

answered Mar 22 at 2:22

daviddavid

597

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Is there a way to fix the problem without splitting the dataset?
  
  – Ronan Venkat
  Mar 22 at 13:13
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Also, how do I break the dataset apart? I can’t find anything on the docs.
  
  – Ronan Venkat
  Mar 22 at 16:40
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() Sorry, I haven't used the dataset in csv form. Generally, I use the api of keras. It has the same api with tensorflow.keras.
  
  – david
  Mar 25 at 2:24
  
  

  
  
  
  

add a comment | 

0

I guess maybe you should split x_train and y_train from Train, and rewrite model.fit as model.fit(x_train, y_train, epochs=epochs, steps_per_epoch=600).

share|improve this answer

answered Mar 22 at 2:22

daviddavid

597

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Is there a way to fix the problem without splitting the dataset?
  
  – Ronan Venkat
  Mar 22 at 13:13
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Also, how do I break the dataset apart? I can’t find anything on the docs.
  
  – Ronan Venkat
  Mar 22 at 16:40
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() Sorry, I haven't used the dataset in csv form. Generally, I use the api of keras. It has the same api with tensorflow.keras.
  
  – david
  Mar 25 at 2:24
  
  

  
  
  
  

add a comment | 

I guess maybe you should split x_train and y_train from Train, and rewrite model.fit as model.fit(x_train, y_train, epochs=epochs, steps_per_epoch=600).

share|improve this answer

answered Mar 22 at 2:22

daviddavid

597

share|improve this answer

answered Mar 22 at 2:22

daviddavid

597

answered Mar 22 at 2:22

daviddavid

597

answered Mar 22 at 2:22

daviddavid

597