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Keras CNN Incompatible with Convolution2D

I am getting into Convolutional Neural Networks and want to create one for MNIST data. Whenever I add a convolutional Layer to my CNN, I get an error:
Input 0 is incompatible with layer conv2d_4: expected ndim=4, found ndim=5
I have attemped to reshape X_Train data set but was not successful
I tried to add a flatten layer first but that returns this error:
Input 0 is incompatible with layer conv2d_5: expected ndim=4, found ndim=2
import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import Flatten, Dense, Dropout
img_width, img_height = 28, 28
mnist = keras.datasets.mnist
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = keras.utils.normalize(X_train, axis=1) #Normalizes from 0-1 (originally each pixel is valued 0-255)
X_test = keras.utils.normalize(X_test, axis=1) #Normalizes from 0-1 (originally each pixel is valued 0-255)
Y_train = keras.utils.to_categorical(Y_train) #Reshapes to allow ytrain to work with x train
Y_test = keras.utils.to_categorical(Y_test)
from sklearn import preprocessing
lb = preprocessing.LabelBinarizer()
Y_train = lb.fit_transform(Y_train)
Y_test = lb.fit_transform(Y_test)
#Model
model = Sequential()
model.add(Flatten())
model.add(Convolution2D(16, 5, 5, activation='relu', input_shape=(1,img_width, img_height, 1)))
model.add(Dense(128, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dropout(.2))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer = 'adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(X_train, Y_train, epochs=3, verbose=2)
val_loss, val_acc = model.evaluate(X_test, Y_test) #Check to see if model fits test
print(val_loss, val_acc)
If I comment out the Convolutional layer, it works very well (accuracy>95%), but I am planning on making a more complex neural network that requires Convolution in the future and this is my starting point

Keras is looking for a tensor of dimension 4 but it's getting ndim as number of dimension as 2.
first make sure you kernel size in Conv2D layer is in parenthesis
model.add(Convolution2D(32, (3, 3), activation='relu', input_shape=(img_height, img_height, 1)))
Second you need to reshape the X_train, X_test variable as Conv2D layer is expecting a tensor input.
X_train = X_train.reshape(-1,28, 28, 1) #Reshape for CNN - should work!!
X_test = X_test.reshape(-1,28, 28, 1)
model.fit(X_train, Y_train, epochs=3, verbose=2)
For more information about Conv2D you can look into Keras Documentation here
Hope this helps.

There are two issues in your code.
You are encoding your labels two times, once using to_categorical, and another time using LabelBinarizer. The latter is no needed here, so just encode your labels into categorical once, using to_categorical.
2.- Your input shape is incorrect, it should be (28, 28, 1).
Also you should add a Flatten layer after the convolutional layers so the Dense layer works properly.

How to convert 1D flattened MNIST Keras to LSTM model without unflattening?

I want to change my model architecture a bit on the LSTM so it accepts the same exact flattened inputs the full connected approach does.
Working Dnn model from Keras examples
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.utils import to_categorical
# import the data
from keras.datasets import mnist
# read the data
(x_train, y_train), (x_test, y_test) = mnist.load_data()
num_pixels = x_train.shape[1] * x_train.shape[2] # find size of one-dimensional vector
x_train = x_train.reshape(x_train.shape[0], num_pixels).astype('float32') # flatten training images
x_test = x_test.reshape(x_test.shape[0], num_pixels).astype('float32') # flatten test images
# normalize inputs from 0-255 to 0-1
x_train = x_train / 255
x_test = x_test / 255
# one hot encode outputs
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
num_classes = y_test.shape[1]
print(num_classes)
# define classification model
def classification_model():
# create model
model = Sequential()
model.add(Dense(num_pixels, activation='relu', input_shape=(num_pixels,)))
model.add(Dense(100, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
# compile model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
return model
# build the model
model = classification_model()
# fit the model
model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, verbose=2)
# evaluate the model
scores = model.evaluate(x_test, y_test, verbose=0)
Same problem but trying LSTM (syntax error still)
def kaggle_LSTM_model():
model = Sequential()
model.add(LSTM(128, input_shape=(x_train.shape[1:]), activation='relu', return_sequences=True))
# What does return_sequences=True do?
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))
opt = tf.keras.optimizers.Adam(lr=1e-3, decay=1e-5)
model.compile(loss='sparse_categorical_crossentropy', optimizer=opt,
metrics=['accuracy'])
return model
model_kaggle_LSTM = kaggle_LSTM_model()
# fit the model
model_kaggle_LSTM.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, verbose=2)
# evaluate the model
scores = model_kaggle_LSTM.evaluate(x_test, y_test, verbose=0)
Problem is here:
model.add(LSTM(128, input_shape=(x_train.shape[1:]), activation='relu', return_sequences=True))
ValueError: Input 0 is incompatible with layer lstm_17: expected
ndim=3, found ndim=2
If I go back and don't flatten x_train and y_train, it works. However, I'd like this to be "just another model choice" that feeds off the same pre-processed input. I thought passing shape[1:] would work as that it the real flattened input_shape. I'm sure it's something easy I'm missing about the dimensionality, but I couldn't get it after an hour of twiddling and debugging, although did figure out not flattening the 28x28 to 784 works, but I don't understand why it works. Thanks a lot!
For bonus points, an example of how to do either DNN or LSTM in either 1D (784,) or 2D (28, 28) would be the best.

RNN layers such as LSTM are meant for sequence processing (i.e. a series of vectors which their order of appearance matters). You can look at an image from top to bottom, and consider each row of pixels as a vector. Therefore, the image would be a sequence of vectors and can be fed to the RNN layer. Therefore, according to this description, you should expect that the RNN layer take an input of shape (sequence_length, number_of_features). That's why when you feed the images to the LSTM network in their original shape, i.e. (28,28), it works.
Now if you insist on feeding the LSTM model the flattened image, i.e. with shape (784,), you have at least two options: either you can consider this as a sequence of length one, i.e. (1, 748), which does not make much sense; or you can add a Reshape layer to your model to reshape back the input to its original shape suitable for the input shape of a LSTM layer, like this:
from keras.layers import Reshape
def kaggle_LSTM_model():
model = Sequential()
model.add(Reshape((28,28), input_shape=x_train.shape[1:]))
# the rest is the same...

ValueError: Error when checking target: expected dense_19 to have 3 dimensions, but got array with shape (5, 3)

I have the following code that creates LSTM network using Keras with TensorFlow backend.
This code runs well.
import numpy as np
import pandas as pd
from sklearn import model_selection
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.utils import np_utils
flights = {
'flight_stage': [1,0,1,1,0,0,1],
'scheduled_hour': [16,16,17,17,17,18,18],
'delay_category': [1,0,2,2,1,0,2]
}
columns = ['flight_stage', 'scheduled_hour', 'delay_category']
df = pd.DataFrame(flights, columns=columns)
X = df.drop('delay_category',1)
y = df['delay_category']
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25, random_state=42)
nb_features = X_train.shape[1]
nb_classes = y.nunique()
hidden_neurons = 32
timestamps = X_train.shape[0]
# Reshape input data to 3D array
X_train = X_train.values.reshape(1, X_train.shape[0], X_train.shape[1])
X_test = X_test.values.reshape(1, X_test.shape[0], X_test.shape[1])
y_train = np_utils.to_categorical(y_train, nb_classes)
y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(LSTM(
units=hidden_neurons,
return_sequences=True,
input_shape=(timestamps,nb_features)
)
)
model.add(Dropout(0.2))
model.add(Dense(activation='softmax', units=nb_classes))
model.compile(loss="categorical_crossentropy",
optimizer='adadelta')
But when I start training the model, it fails:
history = model.fit(X_train, y_train, validation_split=0.25, epochs=500, batch_size=2, shuffle=True, verbose=0)
Error:
ValueError: Error when checking target: expected dense_19 to have 3 dimensions, but got array with shape (5, 3)
This error refers to the final Dense layer. I used model.summary() to get exact dimensions. The output shape of a Dense layer is (None, 5, 3).
However I do not understand why does it have 3 dimensions and what None stands for (how did it appear in this last layer)?

3 is the number of units returned by the last layer. It is the number of classes for the softmax activation
5 is the number of units returned by the lstm which indicates the size of the sequences returned
None is the number of element by batch for the last layer. It simply means that the last layer can accept different size for each batches of tensor of shape [5, 3]
X_train shape: (1, 5, 2),
X_test shape: (1, 2, 2),
y_train shape: (5,3),
y_test shape: (2,3)
Looking at the data shape, there is clearly a mismatch between the batchsize of the features and that of the labels. The most left number should be equal between the features shape X and the labels shape y. It is the batchsize.
'1', 5, 2 => batch size of 1
'2', 3 => batch size of 2
There is a mismatch here.
Also to solve the issue between the output of the lstm layer and the input of the last layer, one can use a layer.flatten
nb_classes = 3
hidden_neurons = 32
model = Sequential()
model.add(LSTM(
units=hidden_neurons,
return_sequences=True,
input_shape=(5, 2)
)
)
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(activation='softmax', units=nb_classes))
model.compile(loss="categorical_crossentropy",
optimizer='adadelta')
model.compile(loss='categorical_crossentropy',
optimizer='adam')
live code

Keras LSTM training. How to shape my input data?

I have a dataset of 3000 observations. Each observation consists of 3 timeseries of length 200 samples. As the output I have 5 class labels.
So I build train as test sets as follows:
test_split = round(num_samples * 3 / 4)
X_train = X_all[:test_split, :, :] # Start upto just before test_split
y_train = y_all[:test_split]
X_test = X_all[test_split:, :, :] # From test_split to end
y_test = y_all[test_split:]
# Print shapes and class labels
print(X_train.shape)
print(y_train.shape)
> (2250, 200, 3)
> (22250, 5)
I build my network using Keras functional API:
from keras.models import Model
from keras.layers import Dense, Activation, Input, Dropout, concatenate
from keras.layers.recurrent import LSTM
from keras.constraints import maxnorm
from keras.optimizers import SGD
from keras.callbacks import EarlyStopping
series_len = 200
num_RNN_neurons = 64
ch1 = Input(shape=(series_len, 1), name='ch1')
ch2 = Input(shape=(series_len, 1), name='ch2')
ch3 = Input(shape=(series_len, 1), name='ch3')
ch1_layer = LSTM(num_RNN_neurons, return_sequences=False)(ch1)
ch2_layer = LSTM(num_RNN_neurons, return_sequences=False)(ch2)
ch3_layer = LSTM(num_RNN_neurons, return_sequences=False)(ch3)
visible = concatenate([
ch1_layer,
ch2_layer,
ch3_layer])
hidden1 = Dense(30, activation='linear', name='weighted_average_channels')(visible)
output = Dense(num_classes, activation='softmax')(hidden1)
model = Model(inputs= [ch1, ch2, ch3], outputs=output)
# Compile model
model.compile(loss='categorical_crossentropy', optimizer=SGD(), metrics=['accuracy'])
monitor = EarlyStopping(monitor='val_loss', min_delta=1e-4, patience=5, verbose=1, mode='auto')
Then, I try to fit the model:
# Fit the model
model.fit(X_train, y_train,
epochs=epochs,
batch_size=batch_size,
validation_data=(X_test, y_test),
callbacks=[monitor],
verbose=1)
and I get the following error:
ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 3 array(s), but instead got the following list of 1 arrays...
How should I reshape my data, to solve the issue?

You magically assume a single input with 3 time series X_train will split into 4 channels and be assigned to different inputs. Well this doesn't happen and that is what the error is complaining about. You have 1 input:
ch123_in = Input(shape=(series_len, 3), name='ch123')
latent = LSTM(num_RNN_neurons)(ch123_in)
hidden1 = Dense(30, activation='linear', name='weighted_average_channels')(latent)
By merging the series together into single LSTM, the model might pickup relations across time series as well. Now your target shape has to be y_train.shape == (2250, 5), the first dimension must match X_train.shape[0].
Another point is you have Dense layer with linear activation, that is almost useless as it doesn't provide any non-linearity. You might want to use a non-linear activation function like relu.

How to prepare multiple RGB images as numpy array for CNN

I want to use the code below with my own input images instead of the mnist images. However I am having a hard time inputting several color .jpg images into a numpy array similar to the X_train used in the code below. I have a folder called data with another folder called train that includes several images that I would like to use as my X_train. I can generate the labels for them and one-hot encode them. I just don't know how to make all my images go into a nice array like X_train. Help? I did look here, but I got a Value Error: setting an array element with a sequence when I just copied and pasted the summarized code.
import numpy as np
np.random.seed(123) # for reproducibility
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist
# 4. Load pre-shuffled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 5. Preprocess input data
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
# 6. Preprocess class labels
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)
# 7. Define model architecture
model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(1,28,28)))
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
# 8. Compile model
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 9. Fit model on training data
model.fit(X_train, Y_train,
batch_size=32, nb_epoch=10, verbose=1)
# 10. Evaluate model on test data
score = model.evaluate(X_test, Y_test, verbose=0)

I am assuming that you are using Theano and that your jpgs have 3 bands. In addition, your jpgs should have the same input shape as the input shape that you indicate in the first convolutional model (28x28 pixels). In that case you can reshape all your jpes with the following lines:
#create random data
no_of_jpgs = 10
jpgs = [np.random.randint(0,255,(28,28,3)) for i in range(no_of_jpgs)]
jpgs = np.array(jpgs)
#reshape data
jpgs.reshape(no_of_jpgs, jpgs.shape[1], jpgs.shape[2], 3)
now you have an array with: (features, n_bands, x, y)
In addition you should change your input_shape so that it supports 3 bands:
model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(3, 28,28)))
If you have jpgs with a different shape or more bands, just change the input_shape values in the first convolutional layer.