I have a signal with 1024 values. When I print it in the console I get this output:
[-13.2172165 -13.0935545 -13.149217 ... -1.8910782 -1.5482559 -1.6714929]
I've been using a very basic autoencoder code from Keras website which looks like this:
encoding_dim = 32
input_signal = Input(shape=(1024,))
encoded = Dense(encoding_dim, activation='relu')(input_signal)
decoded = Dense(1024, activation='sigmoid')(encoded)
autoencoder = Model(input_signal, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
autoencoder.fit(my_signal, epochs=50, shuffle=True)
However, I get this and I don't understand how/why (despite reading intensively the documentation):
ValueError: Error when checking input: expected input_1 to have shape (1024,) but got array with shape (1,)
I've been trying to change the shape input in 1024,1 but then it told me that it expects a 3 dimension input but (here's the weird interpretation): my input is an array with shape (1024,1) even though I didn't change the input at all.
My scenario: I have 2034 arrays (each one with 1024 elements) that I want to fit into my model. For now, I'm trying to make my autoencoder work only with 1 such array. I understand that I need to set the batch size with the amount of my arrays (2034 in this case).
Thanks!
I finally managed to find out my answer: I had to make sure my array is placed in another array (like a 1-D array) like this:
[[-13.2172165 -13.0935545 -13.149217 ... -1.8910782 -1.5482559 -1.6714929]]
I could do it by using:
x_train.reshape((1,1024)).
Related
I have loaded images to train my model on recognizing one feature in those images.
Xtrain is a numpy ndarray of shape (1380,200,200,3 ) containing 1380 images sized 200 by 200pixels in RGB format
Ytrain has targets. shape (1380,2)
When I train my model (model.fit(Xtrain,Ytrain)) I seem to get a value error on everyone of the layers. As if the input was both Xtrain then Ytrain...
ValueError: Error when checking target: expected batch_normalization_24 to have 4 dimensions, but got array with shape (1380, 2)
Image:
The shape of Keras's batch normalizer layer's output is the same as its input. Since you have only two labels, your final layer in a Sequential model should generate two outputs. You can consider adding a Dense layer like:
model.add(Dense(2), activation='relu')
I also recommend to check your model's architecture using print(model.summary()) and make sure that inputs and outputs match with your dataset and vice versa.
I am trying to feed the features extracted from 2 fine-tuned VGG16 (each on a different stream), then for each sequence of 9 data pairs, concatenate their numpy arrays and feed the sequence of 9 outputs (concatenated) to a bi-directional LSTM in Keras.
The problem is that I am running into an error when trying to build the LSTM part. The following shows the generator I wrote to read both RGB and Optical flow streams, extract features and concatenate each pair :
def generate_generator_multiple(generator,dir1, dir2, batch_rgb, batch_flow, img_height,img_width):
print("Processing inside generate multiple")
genX1 = generator.flow_from_directory(dir1,
target_size = (img_height,img_width),
class_mode = 'categorical',
batch_size = batch_rgb,
shuffle=False
)
genX2 = generator.flow_from_directory(dir2,
target_size = (img_height,img_width),
class_mode = 'categorical',
batch_size = batch_flow,
shuffle=False
)
while True:
imgs, labels = next(genX1)
X1i = RGB_model.predict(imgs, verbose=0)
imgs2, labels2 = next(genX2)
X2i = FLOW_model.predict(imgs2,verbose=0)
Xi = []
for i in range(9):
Xi.append(np.concatenate([X1i[i+1],X2i[i]]))
Xi = np.asarray(Xi)
if np.array_equal(labels[1:],labels2)==False:
print("ERROR !! problem of labels matching: RGB and FLOW have different labels")
yield Xi, labels2[2]
I am expecting the generator to yield a sequence of 9 arrays, so the shape of Xi when I force the loop to run twice is: (9, 14, 7, 512)
When I use while True (like in the code above) and try to call the method to check what it returs, after 3 iterations I get the error:
ValueError: too many values to unpack (expected 2)
Now, assuming that there is no problem with the generator, I try to feed the data returned by the generator to the bidirectional LSTM like the following:
n_frames = 9
seq = 100
Bi_LSTM = Sequential()
Bi_LSTM.add(Bidirectional(LSTM(seq, return_sequences=True, dropout=0.25, recurrent_dropout=0.1),input_shape=(n_frames,14,7,512)))
Bi_LSTM.add(GlobalMaxPool1D())
Bi_LSTM.add(TimeDistributed(Dense(100, activation="relu")))
Bi_LSTM.add(layers.Dropout(0.25))
Bi_LSTM.add(Dense(4, activation="relu"))
model.compile(Adam(lr=.00001), loss='categorical_crossentropy', metrics=['accuracy'])
But I keep getting the following error: (the error log is a bit long)
InvalidArgumentError: Shape must be rank 4 but is rank 2 for 'bidirectional_2/Tile_1' (op: 'Tile') with input shapes: [?,7,512,1], [2].
It seems to be caused by this line:
Bi_LSTM.add(Bidirectional(LSTM(seq, return_sequences=True, dropout=0.25, recurrent_dropout=0.1),input_shape=(n_frames,14,7,512)))
I am not sure anymore if the problem is the way I try to build the LSTM, the way I return the data from the generator, or the way I define the input of LSTM.
Thanks a lot for any help you can provide.
It seems like this error specifically is cause by the following line:
input_shape=(n_frames,14,7,512)
I was confused about the input for LSTM. Instead to explicitly giving the shape of the input, we just need to specify the dimensions of the input. In my case, this is 3 since the input is a 3D np array. I still have other problems with my code, but for this specific error, the solution is changing that part with:
input_shape=(n_frames,3)
Edit:
When predicting, We need to get the mean of the prediction since LSTM expects a 1D input.
Another issue in my code was the shape of Xi. It needs to be reshaped before yielding it so that it matches the input expected by LSTM.
My Keras RNN code is as follows:
def RNN():
inputs = Input(shape = (None, word_vector_size))
layer = LSTM(64)(inputs)
layer = Dense(256,name='FC1')(layer)
layer = Dropout(0.5)(layer)
layer = Dense(num_classes,name='out_layer')(layer)
layer = Activation('softmax')(layer)
model = Model(inputs=inputs,outputs=layer)
return model
I'm getting the error when I call model.fit()
model.fit(np.array(word_vector_matrix), np.array(Y_binary), batch_size=128, epochs=10, validation_split=0.2, callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)])
Word_vector_matrix is a 3-dim numpy array.
I have printed the following :
print(type(word_vector_matrix), type(word_vector_matrix[0]), type(word_vector_matrix[0][0]), type(word_vector_matrix[0][0][0]))
and the answer is :
<class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.float32'>
It's shape is 1745 x sentence length x word vector size.
The sentence length is variable and I'm trying to pass this entire word vector matrix to the RNN, but I get the error above.
The shape is printed like:
print(word_vector_matrix.shape)
The answer is (1745,)
The shape of the nested arrays are printed like:
print(word_vector_matrix[10].shape)
The answer is (7, 300)
The first number 7 denotes the sentence length, which is variable and changes for each sentence, and the second number is 300, which is fixed for all words and is the word vector size.
I have converted everything to np.array() as suggested by the other posts, but still the same error. Can someone please help me. I'm using python3 btw. The similar thing is working in python2 for me, but not in python3. Thanks!
word_vector_matrix is not a 3-D ndarray. It's a 1-D ndarray of 2-D arrays. This is due to variable sentence length.
Numpy allows ndarray to be list-like structures that may contain a complex element (another ndarray). In Keras however, the ndarray must be converted into a Tensor (which has to be a "mathematical" matrix of some dimension - this is required for the sake of efficient computation).
Therefore, each batch must have fixed size sentences (and not the entire data).
Here are a few alternatives:
Use batch size of 1 - simplest approach, but impedes your network's convergence. I would suggest to only use it as a temporary sanity check.
If sequence length variability is low, pad all your batches to be of the same length.
If sequence length variability is high, pad each batch with the max length within that batch. This would require you to use a custom data generator.
Note: After you padded your data, you need to use Masking, so that the padded part will be ignored during training.
I am using tf-idf vector data as an input for my Keras model. tf-idf vectors has the following shape:
<class 'scipy.sparse.csr.csr_matrix'> (25000, 310617)
Code:
inputs = Input((X_train.shape[1],))
convnet1=Conv1D(128,3,padding='same',activation='relu')(inputs)
Error:
ValueError: Input 0 is incompatible with layer conv1d_25: expected ndim=3, found ndim=2
When I am converting the input to Input(None,X_train.shape[1],) then I am getting an error while fitting because the input dimension has been changed to 3.
As mentioned in the error (i.e. expected ndim=3, found ndim=2), Conv1D takes a 3D array as input. So if you would like to feed this array to it you first need to reshape it:
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
And also set the input layer shape accordingly:
inputs = Input(X_train.shape[1:])
However, Conv1D is usually used for processing sequences (like sequence of words in a sentence) or temporal data (like timeseries of weather temperature). And that's exactly why it takes inputs with shape of (num_samples, num_timesteps or sequence_len, num_features). Applying it on a tf-idf representation, which does not have any sequential order, may not be efficient that much. Instead, I suggest you to use a Dense layer. Or alternatively, instead of using tf-idf you can also feed the raw data (i.e. texts or sentences) directly into an Embedding layer and use Conv1D or LSTM layer(s) after it.
I am trying to use an LSTM model for binary classification on multivariate time series data. I have seven properties collected over the course of the day for about 100 days (i.e. 100 arrays of size [9000, 7]). Each of these arrays has a single classification status of either 1 or 0.
I've started out trying to build the simplest model possible given that I am new to Keras and Machine Learning in general, but I keep getting errors regarding input shape when I try to train them. For example, my first layers:
model = Sequential()
model.add(Conv2D(32, (3,3), input_shape=(9000,7,1), activation='relu'))
...
model.fit(x=X_train, y=Y_train, epochs=100)
With an X_train of type float64, and a size of (100L, 9000L, 7L), I get an error that reads:
ValueError: Error when checking input: expected conv2d_11_input to have 4 dimensions, but got array with shape (100L, 9000L, 7L)
I've tried changing the batch size and number of epochs with no sucess so can someone explain how to correctly reshape my input? Am I missing something simple?
I suspect you want to use a Conv1D (3D data), no?
You're using a Conv2D (4D data = images).
For either Conv1D and any of the RNN layers, such as LSTM, your input is 3D data and your input_shape should be input_shape=(9000,7).
The input data should be an array with shape (100,9000,7), which is already ok, by the content of the error message.
Assuming each day is an individual sequence and you don't want to connect days.