Im trying to build network like this:
the network
and my question is how to implement the begining with the shared weights,
because it contains FC+BN+ReLu (3-layers), and I have multi inputs vectors( M(~25) vectors with length=F).
I tried with functional API modle in keras, and I had some diffculty with this.
thanks
You can try using a TimeDistributed over each layer.
For example:
model = Sequential()
model.add(TimeDistributed(MobileNetV2(weights='imagenet',include_top=False), input_shape=(n_sequence, *dim, n_channels)))
model.add(TimeDistributed(GlobalAveragePooling2D()))
model.add(CuDNNLSTM(64, return_sequences=False))
model.add(Dense(64, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(24, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(n_output, activation='softmax'))
Code has been taken from https://github.com/peachman05/action-recognition-tutorial/blob/master/model_ML.py
Related
Good morning,
i tried to use a sequential model to create my neural network which have a multiple input (concatenated). But i want to know if shall i use The Keras functional API to CREATE my model.
in1= loadtxt('in1.csv', delimiter=',')#2D matrix
in2= loadtxt('in2.csv', delimiter=',')#2D matrix
y= loadtxt('y.csv', delimiter=',') #2D matrix (output labels)
X_train=np.hstack((in1,in2))
y_train=y
model = Sequential()
model.add(Dense(nbinneuron, input_dim=2*nx,activation='tanh',kernel_initializer='normal'))
model.add(Dropout(0.5))
#output layer
model.add(Dense(2, activation='tanh'))
opt =Adalta(lr=0.01)
model.compile(loss='mean_squared_error', optimizer=opt, metrics=['mse'])
# fit the keras model on the dataset
history=model.fit(X_train, y_train,validation_data=(X_test, y_test), epochs=500,verbose=0)
...
thanks an advance
A Sequential Model can only have one input and one output. To build a model with multiple inputs (and/or multiple outputs), you need to use the Functional API.
I'm using two differents neural networks sequential models in my python program.
One RNN model defined like this:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, CuDNNLSTM
ModelRNN = Sequential()
ModelRNN.add(CuDNNLSTM(150, return_sequences=True, batch_size=None, input_shape=(None,10)))
ModelRNN.add(CuDNNLSTM(150, return_sequences=True))
ModelRNN.add(Dense(100, activation='relu'))
ModelRNN.add(Dense(10, activation='relu'))
optimizer = tf.keras.optimizers.Adam(lr=0.001)
ModelRNN.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['accuracy'])
One Dense model defined like this :
ModelDense = Sequential()
ModelDense.add(Dense(380, batch_size=None, input_shape=(1,380), activation='elu'))
ModelDense.add(Dense(380, activation='elu'))
ModelDense.add(Dense(380, activation='elu'))
ModelDense.add(Dense(380, activation='elu'))
optimizer = tf.keras.optimizers.Adam(lr=0.00025)
ModelDense.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['accuracy'])
So my problem is that the two networks will work together so i have to run both of them in the same tensorflow session BUT, i want to save them in two differents folders.
I don't really know if it's possible because i never really interested myself in how is working the tensorflow graphs at all and i only know that when i use my tensorflow saver, i only give as parameter my session and a path.
So my question is how can i separate the storage of my models in two folders ?
I want to do that because i want to be able to easily change my RNN without having to retrain both of my networks or without having to overwrite my trained RNN
If i'am not clear please ask me for more details
So my question is how can i separate the storage of my models in two
folders ?
The save method of Keras model accepts a file path, so different folders can be given for the individual models.
ModelRNN.save('folder1/<filename.h5>')
ModelDense.save('folder2/<filename.h5>')
https://www.tensorflow.org/tutorials/keras/save_and_restore_models#save_the_entire_model
I am currently training a net to play a game with a CNN having the following architecture:
model = Sequential()
model.add(Conv2D(100, kernel_size=(2, 2), strides=(2, 2), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(250, activation='relu'))
model.add(Dense(classifications, activation='softmax'))
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=['accuracy'])
Now I wish to introduce some complexity in the architecture and make the net deep. How can I tabulate the performance of the CNNs of different complexities and ultimately conclude by giving the best choice for the particular task?
Am i going in the wrong direction? How to decide the depth of a CNN and how does it affect the performance on the same dataset?
Thanks in advance (I am new to this site, kindly excuse the immaturity of this post)
Edit: Information about the dataset I am using: dataset consists of images and each image has 3 possible lables (0, 1, 2) stored in a CSV file with each row corresponding to that particular image.
The simplest thing you can do is generate a few different model architectures, train them on a train set and evaluate them on the test set. Then compare their accuracies and the one with the highest accuracy should in theory be the best performing model.
To make the model deeper you can add extra dense or convolutional layers. For example:
changing this:
model.add(Dense(250, activation='relu'))
to this:
model.add(Dense(250, activation='relu'))
model.add(Dense(250, activation='relu'))
model.add(Dense(250, activation='relu'))
will add three extra dense layers. Hence making the network deeper.
You can do the same with duplicating the convolutional layers by duplicating the Conv2D and MaxPooling2D lines.
The alternative to 'trial and error' approach to finding the best architecture and hyperparameters is to use a search approach like explained in this tutorial that user grid search. It will, however, take significantly longer than just trying out a few versions you can up with yourself.
I'm going crazy in this project. This is multi-label text-classification with lstm in keras. My model is this:
model = Sequential()
model.add(Embedding(max_features, embeddings_dim, input_length=max_sent_len, mask_zero=True, weights=[embedding_weights] ))
model.add(Dropout(0.25))
model.add(LSTM(output_dim=embeddings_dim , activation='sigmoid', inner_activation='hard_sigmoid', return_sequences=True))
model.add(Dropout(0.25))
model.add(LSTM(activation='sigmoid', units=embeddings_dim, recurrent_activation='hard_sigmoid', return_sequences=False))
model.add(Dropout(0.25))
model.add(Dense(num_classes))
model.add(Activation('sigmoid'))
adam=keras.optimizers.Adam(lr=0.04)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])
Only that I have too low an accuracy .. with the binary-crossentropy I get a good accuracy, but the results are wrong !!!!! changing to categorical-crossentropy, I get very low accuracy. Do you have any suggestions?
there is my code: GitHubProject - Multi-Label-Text-Classification
In last layer, the activation function you are using is sigmoid, so binary_crossentropy should be used. Incase you want to use categorical_crossentropy then use softmax as activation function in last layer.
Now, coming to the other part of your model, since you are working with text, i would tell you to go for tanh as activation function in LSTM layers.
And you can try using LSTM's dropouts as well like dropout and recurrent dropout
LSTM(units, dropout=0.2, recurrent_dropout=0.2,
activation='tanh')
You can define units as 64 or 128. Start from small number and after testing you take them till 1024.
You can try adding convolution layer as well for extracting features or use Bidirectional LSTM But models based Bidirectional takes time to train.
Moreover, since you are working on text, pre-processing of text and size of training data always play much bigger role than expected.
Edited
Add Class weights in fit parameter
class_weights = class_weight.compute_class_weight('balanced',
np.unique(labels),
labels)
class_weights_dict = dict(zip(le.transform(list(le.classes_)),
class_weights))
model.fit(x_train, y_train, validation_split, class_weight=class_weights_dict)
change:
model.add(Activation('sigmoid'))
to:
model.add(Activation('softmax'))
I am trying to build a stateful LSTM with Keras and I don't understand how to add a embedding layer before the LSTM runs. The problem seems to be the stateful flag. If my net is not stateful adding the embedding layer is quite straight forward and works.
A working stateful LSTM without embedding layer looks at the moment like this:
model = Sequential()
model.add(LSTM(EMBEDDING_DIM,
batch_input_shape=(batchSize, longest_sequence, 1),
return_sequences=True,
stateful=True))
model.add(TimeDistributed(Dense(maximal_value)))
model.add(Activation('softmax'))
model.compile(...)
When adding the Embedding layer I move the batch_input_shape parameter into the Embedding layer i.e. only the first layer needs to known the shape?
Like this:
model = Sequential()
model.add(Embedding(vocabSize+1, EMBEDDING_DIM,batch_input_shape=(batchSize, longest_sequence, 1),))
model.add(LSTM(EMBEDDING_DIM,
return_sequences=True,
stateful=True))
model.add(TimeDistributed(Dense(maximal_value)))
model.add(Activation('softmax'))
model.compile(...)
The exception I get know is Exception: Input 0 is incompatible with layer lstm_1: expected ndim=3, found ndim=4
So I am stuck here at the moment. What is the trick to combine word embeddings into a stateful LSTM?
The batch_input_shape parameter of the Embedding layer should be (batch_size, time_steps), where time_steps is the length of the unrolled LSTM / number of cells and batch_size is the number of examples in a batch.
model = Sequential()
model.add(Embedding(
input_dim=input_dim, # e.g, 10 if you have 10 words in your vocabulary
output_dim=embedding_size, # size of the embedded vectors
input_length=time_steps,
batch_input_shape=(batch_size,time_steps)
))
model.add(LSTM(
10,
batch_input_shape=(batch_size,time_steps,embedding_size),
return_sequences=False,
stateful=True)
)
There is an excellent blog post which explains stateful LSTMs in Keras. Also, I've uploaded a gist which contains a simple example of a stateful LSTM with Embedding layer.