I am a new hand of using Keras. After searching without results. Please Please save me!
Here is my problem:
print(input_shape)
X_input = Input(input_shape)
print(X_input)
result in
(600, 64, 64, 3)
Tensor("input_5:0", shape=(?, 600, 64, 64, 3), dtype=float32)
It add one dimension autoly and error
ValueError: Input 0 is incompatible with layer conv0: expected ndim=4, found ndim=5
Your problem lies in the fact that in keras a first dimension is connected to a number of samples (I guess it's 600 in your case) and it's skipped while defining an input shape. So try:
X_input = Input(input_shape[1:])
in order to skip a sample dimension.
Related
I am trying to work with a Simple RNN to predict Parkinson's Gait using Physionet Database. I am feeding the RNN with Images of height 240 and width of 16 pixels. I am also using Model checkpoint and monitor validation accuracy to save the best weights. While trying the input shape to the RNN I am getting that error as
ValueError: Input 0 of layer sequential_13 is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: (None, None, None, None)
RNN model:
model = Sequential()
model.add(SimpleRNN(24, kernel_initializer='glorot_uniform', input_shape=(64,240), return_sequences = True))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(2))
model.add(Activation('softmax'))
opt = optimizers.RMSprop(learning_rate=0.001, decay=1e-6)
epoch=10
early_stopping = EarlyStopping(monitor='val_accuracy', patience=60, verbose=1, mode='auto')
checkpoint = ModelCheckpoint("model_parkinsons.h5",
monitor='val_accuracy', verbose=0, save_best_only=True,
save_weights_only=False, mode='auto', save_freq='epoch')
model.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=['accuracy'])
Batch size:64
Height of the image: 240
a.shape
Output: (64, 16, 240, 1)
I tried to feed the input shape as a.shape[1:]
But I am getting the error as expected 3 dimension but got 4 dimension.
Please help me how to resolve this.
In your first layer, you specified the input shape of your network. This shape does not include your batch size. So, if you specify "input_shape=(64, 240)", this would mean that your final input would need to have the shape (batch_size, 64, 240). Since 64 is your batch size, it seems like there definitely went something wrong there. Additionally, your input has four dimensions: (64, 16, 240, 1), but your first layer takes three dimensional inputs. I do not quite understand what you want to achieve with your model, but it should work if you input a[:, :, :, 0] into your model instead of a. Additionally, you need to set "input_shape=(16, 240)" in your first layer. If you do these two things, then your model uses the RNN to process one column of the image at a time. This approach does not make any sense to me (since RNNs are not used for image processing, at least not in this form), but I do not see any other way to interpret what you already did.
I am new to tensor flow and i am trying to build a multivariate (two features for each time step) multi step (forecast 12 time step in the future) forecast model.
I created tensorflow data set to feed it to my model:
When i print the shape of my data set, i find the following:
<PrefetchDataset shapes: ((None, None, 2), (None, None)), types: (tf.float32, tf.float32)>
This is what i understand:
(None, None, 2) = Input tensor "features" : (batchSize, Timesteps input, Features by time step)
(None, None) =Output Tensor "label" (batchSize, future forecsated time steps )
I follow up by creating my model as following :
keras.backend.clear_session()
tf.random.set_seed(42)
np.random.seed(42)
model = keras.models.Sequential([
keras.layers.SimpleRNN(50),
keras.layers.SimpleRNN(100),
keras.layers.Dense(12),
])
optimizer = keras.optimizers.SGD(lr=1.5e-6, momentum=0.9)
model.compile(loss="mae",
optimizer=optimizer,
metrics=["mae"])
When i fit the model
model.fit(train_set, epochs=5,
validation_data=valid_set)
I have the following error:
ValueError: Input 0 of layer simple_rnn_1 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: [None, 50]
I do understand that SimpleRNN layer is expecting a 3 dimension tensor. But i think that my input has this dimension.
Thanks a lot for the help.
If you need me to share with you how i am creating my dataset, i would gladly do it.
The issue was coming form the second layer not the first one. Basically, the activation of the first layer issue a vector rather than a sequence, so it will have a rank of 2, eg: (a, b). But the second layer requires a three dimension input. TO solve this i added the return_sequences=True in the first layer of RNN.
If train_set is a numpy array, pass train_set.reshape((1,50)) to model.fit()
model.fit(train_set.reshape((1,50)), epochs=5,
validation_data=valid_set)
Then you wouldn't need to apply return_sequences=True to the first RNN cell either.
I'm making an autoEncoder for depth estimation from monocular images. The first layer is a convolutional layer and the second layer is a convolutional LSTM layer. How do I add the ConvLSTM2D layer after the Conv2D layer.
This is the code I've tried but it gives an error.
autoencoder = Sequential()
autoencoder.add(Conv2D(64, (3, 3),strides = 2 , input_shape = (640, 480, 3), activation = 'linear'))
autoencoder.add(LeakyReLU(alpha = 0.1))
autoencoder.add(ConvLSTM2D(256, (3,3), strides = 2, input_shape = (None, 32), return_sequences = True))
I get the following error
ValueError: Input 0 is incompatible with layer conv_gr_u2d_1: expected
ndim=5, found ndim=4
You have maybe misunderstood what ConvLSTM2D is good for. It is designed for the scenario that you have a series of data where each data point is a picture. So, a movie would be a typical use case.
So, whatever you feed into it must have the shape (batch_size, timesteps, rows, cols, channels). On the other hand, Conv2D has an output shape of (batch_size, rows, cols, features). This is what the error is telling you.
Technically, you could just add a Reshape layer between those and generate whatever shape you want, but I don't see how this would make any sense in your scenario.
Having it vice versa (ConvLSTM2D first, then Conv2D) would make much more sense. But then you need "movie-like" input data. If I understand you correctly, you don't have that.
input shape to Conv2D should be:
input_shape = (batch_size, img_wd, img_hg, channels)
eg:
input_shape = (None, 640, 480, 3)
and u dont have to add input_shape argument in ConvGRU2D
I'm working on a speech recognition problem running on Colab using LSTM. The audio files were converted into spectrograms and then normalized. There are 6840 spectrograms in total and the shape of each one is (288, 864, 4).
I already tried a few examples with RNN and CNN and they worked, but when I try an example using a LSTM I get shape errors, every time either there is one more or one less dimension than expected. Here are some of these cases :
rnn = keras.Sequential()
rnn.add(keras.layers.SimpleRNN(500, input_shape = (864, 4)))
rnn.add(keras.layers.LSTM(500, return_sequences = True))
rnn.add(keras.layers.Dropout(0.2))
rnn.add(keras.layers.LSTM(500, return_sequences = True))
rnn.add(keras.layers.Dropout(0.2))
rnn.add(keras.layers.LSTM(500, return_sequences = True))
rnn.add(keras.layers.Dropout(0.2))
rnn.add(keras.layers.Dense(212, activation = 'softmax'))
rnn.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy',metrics = ['accuracy'])
rnn.fit(X_train, y_train, epochs = 5, validation_data=(X_test, y_test))
scores = rnn.evaluate(X_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', '%.2f' % (scores[1] * 100), '%')
The following error is raised on the first LSTM layer : ValueError: Input 0 of layer lstm_54 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: [None, 500]
If I remove the SimpleRNN line and feed the input directly to the first LSTM like this
rnn.add(keras.layers.LSTM(500, return_sequences = True, input_shape = (288, 864, 4)))
I get : ValueError: Input 0 of layer lstm_56 is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, 288, 864, 4]
I tried reshaping the images to (4, 288 * 864) and got the same error when trying to use the RNN layer, but with just the LSTM I got InvalidArgumentError: Incompatible shapes: [32] vs. [32,4].
No idea where the 32 came from, though.
One last thing, not really an issue but more of a request, is there any library that can resize images the simple way? 288x864 is too big for Colab, so I'll have to do it eventually to be able to load all 6840 images and feed it to the neural network. Right now I'm just using 100 samples to test.
Feel free to leave suggestions about other methods, cabalistic number of nodes/layers or anything like that.
LSTM input is 3 dimensions [n_samples, n_timesteps, n_features], so your first line also need to enable return sequences:
rnn.add(keras.layers.SimpleRNN(500, return_sequences = True, input_shape = (864, 4)))
Next, your Dense layer will complain from wrong input size, so you want to remove return_sequence on the last LSTM network:
rnn.add(keras.layers.LSTM(500))
If you still want to keep the return_sequences = True on the last LSTM layer, you might want to wrap the Dense layer in a TimeDistributed.
I tried it on the following input and they seems to work
X_train = np.random.rand(100, 864, 4)
y_train = np.random.rand(100, 1)
The PIL from pillow package has plenty of image manipulation methods.
I am trying to classify handwritten digits using the MNIST dataset to train my model. My model trained successfully and hit an accuracy of 98.9%. But when I try and input a custom image it shows me the following error :
Error when checking : expected conv2d_4_input to have shape (None, 28, 28, 1) but got array with shape (1, 1, 28, 28)
This is the first convolutional layer i.e. the input layer.
What can I do to resolve this issue ?
This is my Convolutional Neural Network :
conv_model = Sequential()
conv_model.add(Conv2D(filters, kernel_size[0], input_shape=(28 , 28 , 1)))
conv_model.add(Activation(act))
conv_model.add(Conv2D(filters, kernel_size[0]))
conv_model.add(Activation(act))
conv_model.add(MaxPool2D(pool_size=(2,2)))
conv_model.add(Dropout(0.25))
conv_model.add(Flatten())
conv_model.add(Dense(128))
conv_model.add(Activation(act))
conv_model.add(Dropout(0.5))
conv_model.add(Dense(10))
conv_model.add(Activation('softmax'))
#conv_model.summary()
Compilation Details :
conv_model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])
COMPLETE SOURCE CODE :
https://github.com/tanmay-edgelord/HandwrittenDigitRecognition
The image :
If any further details are required please comment.
The error message is pretty straight:
Your first layer is expecting data with shape (None, 28, 28, 1), where "None" can be any number (it's the batch size, how many examples you have).
Your data on the other hand has shape (1, 1, 28, 28).
The confusion seems to me a common one: Keras puts the channels at the last dimension, and your data has the channels in the first.
Solution:
Just reshape your data in the correct format: (1, 28, 28, 1).
But are you trying to give that entire image to the model??? If so, it won't work very well, it's expecting images with 28 x 28 pixels.
You will have to separate each number in a different 28 x 28 image. And you must take into account the possibility of your image being inverted in terms of what is black and what is white. Usually the MNIST data has a black background (0 values) with a white number (1 values).
The problem got solved by passing it to the reshape function with the correct input size
roi2 = roi.reshape(1,28,28,1)