I have trained a keras LSTM model. But after training, all i get is the final parameters of the models after training with 10 epochs and batch size=120. How can i get intermediate parameter after a batch keras.
Example: after 120 sample in each batch i can get the intermediate parameter of this step.
I have tried callback method and backend in keras, but i do not know how to get the
'''python
model = Sequential()
model.add(Embedding(max_features, 32))
#model.add(LSTM(32, return_sequences=True, input_shape=(1,texts.shape[0])))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])
history_ltsm = model.fit(texts_train, y_train, epochs=10, batch_size=120, validation_split=0.2)
'''
I expected the model run step by step based on each batch to show the intermediate parameters, but not the all epochs.
Thank you very much!
Related
How to get the perfect copy of this Keras sequential network in PyTorch?
model = Sequential()
model.add(Masking(mask_value=-99., input_shape=(sequence_length, train_array.shape[2])))
model.add(LSTM(32, activation='tanh'))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam') # the model is recompiled to reset the optimizer
model.load_weights('simple_lstm_weights.h5') # weights are reloaded to ensure reproducible results
history = model.fit(train_split_array, train_split_label,
validation_data=(val_split_array, val_split_label),
epochs=5,
batch_size=32)
How to get the perfect copy of this Keras sequential network in PyTorch?
I am new to Keras and trying to write my first code. I want to understand what 'model.predict' should return. Consider a simple model below.
model = keras.Sequential()
model.add(keras.layers.Dense(12, input_dim=232, activation='relu'))
model.add(keras.layers.Dense(232, activation='relu'))
model.add(keras.layers.Dense(1, activation='sigmoid'))
# compile the keras model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit the keras model on the dataset
model.fit(vSignal, vLabels, epochs=15, batch_size=100 )
# evaluate the keras model
_, accuracy = model.evaluate(vSignal, vLabels)
print('Accuracy: %.2f' % (accuracy*100))
pred=model.predict(vSignalT)
Consider we train the "model" with "vSignal" and "vLabels" as shown above. Now consider that the accuracy of the model as given by model.evaluate is 100%. Now if we give same data 'vSignal' to 'model.predict' should we get the 'vLabels' return?
pred=model.predict(vSignalT) returns a numpy arrays of predictions.
each row consists of one of the vlabels that the model predicted.
for more information refer to here
save return value of fit function:
hist = model.fit(vSignal, vLabels, epochs=15, batch_size=100 );
then check the
hist.history["accuracy"]
I was trying to train a model using IMDB data. I am getting expected train accuracy about 96%+ but I am not satisfied with the test accuracy.Now my expectation is to get 90%+ test accuracy on test data. I tried by using several classifier but each time I am getting 84% to 89% accuracy on test data. Here I am going to include some classifiers I already tried. Most of the cases I tried some parameter tuning by increasing epoch or changing the optimizer. Now my concern is how can I increase the test accuracy to 90%+ .
Classifiers I tried so far:
First:
model = Sequential()
model.add(Embedding(vocab_size, 32, input_length = max_words))
model.add(Bidirectional(LSTM(32, return_sequences = True)))
model.add(GlobalMaxPool1D())
model.add(Dense(20, activation="relu"))
model.add(Dropout(0.05))
model.add(Dense(1, activation="sigmoid"))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train,y_train,validation_data=(x_test, y_test),epochs=10,batch_size=100)
Second:
model = Sequential([
Embedding(vocab_size, 32, input_length=max_words),
Dropout(0.2),
ZeroPadding1D(padding=1),
Convolution1D(64, 5, activation='relu'),
Dropout(0.2),
MaxPooling1D(),
Flatten(),
Dense(100, activation='relu'),
Dropout(0.2),
Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train,y_train,validation_data=(x_test, y_test),epochs=10,batch_size=100)
By checking on State-of-the-art analysis on IMDB dataset, I don't think you can get to ^90% with simple models like those you are using. However, you may try using pretrained embedding like glove instead of training your own embedding. Also, I found this repo have BERT implementation in keras, providing demo of IMBD classification, it is able to get ~99% acc.
I don't understand input of RNN model. Why it show None before node size every layer. Why it is (None,1) (None,12)
This is my code.
K.clear_session()
model = Sequential()
model.add(Dense(12, input_dim=1, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
This is not a RNN, it's just a fully connected network (FC or Dense).
The first dimension of every tensor in a Keras network is the batch_size, which represents the number of "samples" or "examples" you are passing to the model. The value is None because this dimension is not fixed, you can have batches of any size you want.
I modified the existing activation function and using it in the Convolutional layer of the Neural Network. I would like to know how does it perform compared to the existing activation function.Is there any method/function to plot in a graph the results(matrix values) after each Neural network layer,so that I could customise my activation function according to the values for better results?
model = Sequential()
e = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=max_length, trainable=False)
model.add(e)
model.add(Conv1D(64,kernel_size,padding='valid',activation=newactivation,strides=1))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(Conv1D(256,kernel_size,padding='valid',activation=newactivation,strides=1))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(Bidirectional(GRU(gru_output_size, dropout=0.2, recurrent_dropout=0.2)))
model.add(Bidirectional(LSTM(lstm_output_size)))
model.add(Dense(nclass, activation='softmax'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
print(model.summary())
model.fit(padded_docs,y_train, epochs=epoch_size, verbose=0)
loss, accuracy = model.evaluate(tpadded_docs, y_test, verbose=0)
I cannot comment yet so I post this as an answer:
Refer to the Keras FAQ: "How can I obtain the output of an intermediate layer?"
It shows you how you can access the output of each layer. If you are using the version that uses the keras function, you can even access the output of the model in the learning phase (if your model contains layer that behave differently in training vs. testing).