i am trying to build a deep learning network based on LSTM RNN
here is what is tried
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM
import numpy as np
train = np.loadtxt("TrainDatasetFinal.txt", delimiter=",")
test = np.loadtxt("testDatasetFinal.txt", delimiter=",")
y_train = train[:,7]
y_test = test[:,7]
train_spec = train[:,6]
test_spec = test[:,6]
model = Sequential()
model.add(LSTM(32, input_shape=(1415684, 8)))
model.add(LSTM(64, input_dim=1, input_length=1415684, return_sequences=True))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
model.fit(train_spec, y_train, batch_size=2000, nb_epoch=11)
score = model.evaluate(test_spec, y_test, batch_size=2000)
but it gets me the following error
ValueError: Input 0 is incompatible with layer lstm_2: expected ndim=3, found ndim=2
Here is a sample from the dataset
(Patient Number, time in millisecond, accelerometer x-axis,y-axis, z-axis,magnitude, spectrogram,label (0 or 1))
1,15,70,39,-970,947321,596768455815000,0
1,31,70,39,-970,947321,612882670787000,0
1,46,60,49,-960,927601,602179976392000,0
1,62,60,49,-960,927601,808020878060000,0
1,78,50,39,-960,925621,726154800929000,0
in the dataset i am using the only the spectrogram as input feature and the label (0 or 1) as the output
the total traing samples is 1,415,684
Related
I am using the following code to apply sequential LSTM to time-series data with one value. It works fine with a Keras version. I am wondering how could I do the same using PyTorch?
import tensorflow
from tensorflow.keras import optimizers
from tensorflow.keras import losses
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, Input, Dropout, Embedding, LSTM
from tensorflow.keras.optimizers import RMSprop, Adam, Nadam
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.callbacks import TensorBoard
# training_dataset.shape = (303, 24, 1)
time_steps = 24
metric = 'mean_absolute_error'
model = Sequential()
model.add(LSTM(units=32, activation='tanh', input_shape=(time_steps, 1), return_sequences=True))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='mean_absolute_error', metrics=[metric])
print(model.summary())
batch_size=32
epochs=20
model.fit(x=training_dataset, y=training_dataset,
batch_size=batch_size, epochs=epochs,
verbose=1, validation_data=(training_dataset, training_dataset),
callbacks=[TensorBoard(log_dir='../logs/{0}'.format(tensorlog))])
testing_pred = model.predict(x=testing_dataset)
You can check the pytorch documentation for that: https://pytorch.org/docs/master/generated/torch.nn.LSTM.html
the simplest code is the following:
import torch, torch.nn as nn, torch.optim.Adam as Adam
model = nn.Sequential(nn.LSTM(input_size=1, hidden_size=32, output_size=1), nn.Sigmoid)
opt = Adam(model.parameters())
loss_func = nn.MSELoss()
for (x, y) in dataloader:
opt.zero_grad()
pred = model(x)
loss = loss_func(y, pred)
loss.backward()
opt.step()
I am trying to train a convolutional neural network on google colab for a medical classification problem. The data set is 89 256x256x256 images for training and 11 for testing. When I try to make my model train it gives me the following error:
import keras
from keras import optimizers
import keras.models
from keras.models import Sequential
import keras.layers
from keras.layers.convolutional import Conv3D
from keras.layers.convolutional import MaxPooling3D
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers import Dense
from keras import metrics
model = Sequential()
model.add(Conv3D(64, kernel_size=(3,3,3),
activation='relu',
input_shape=(10,1,256,256,256)))
model.add(Conv3D(64, (2,2,2), activation='relu'))
model.add(MaxPooling3D(pool_size=(2,2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
opt=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model.compile(opt, loss='categorical_crossentropy', metrics=['mae','acc'])
model.fit(x=train_data, y=train_labels,epochs=100, batch_size=10, verbose=2 ,callbacks=None, validation_split=0.0, validation_data=(validation_data,validation_labels), shuffle=True)
This is the error i get:
ValueError: Input 0 is incompatible with layer conv3d_56: expected ndim=5, found ndim=6
Assuming you are using channels first data_format, your input_shape argugment to the first Conv3D layer should be (CHANNELS, HEIGHT, WIDTH, DEPTH). But your input shape tuple has length of 5, and that is not what Conv3D layer expecting. Assuming the batch_size(of 10) is specified by mistake, making the following changes should fix the problem
model.add(Conv3D(64, kernel_size=(3,3,3),
activation='relu',
input_shape=(1,256,256,256)))
Edit
If you are using channels_last data-format your input_shape should be (HEIGHT, WIDTH, DEPTH, CHANNELS). And assuming your images have 1 channels, the above line should be,
model.add(Conv3D(64, kernel_size=(3,3,3),
activation='relu',
input_shape=(256,256,256, 1)))
I am newbie on keras,
I try to follow the Keras tutorial for Multilayer Perceptron (MLP) for multi-class softmax classification, using my data set.
My data has 3 classes and only one feature, but I don't understand why the result always show just 0,3 of accuracy and the model predicted all training data as first class. then the confusion matrix is like this.
Confusion matrix
Here the coding:
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
import pandas as pd
import numpy as np
# Importing the dataset
dataset = pd.read_csv('StatusAll.csv')
X = dataset.iloc[:, 1:].values
y = dataset.iloc[:, 0:1].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
from keras.utils import to_categorical
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
model = Sequential()
# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.
model.add(Dense(64, activation='tanh', input_dim=1))
model.add(Dropout(0.5))
model.add(Dense(64, activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(4, activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
history = model.fit(x_train, y_train,
epochs=100,
batch_size=128)
score = model.evaluate(x_test, y_test, batch_size=128)
print('Test score:', score[0])
print('Test accuracy:', score[1])
from sklearn import metrics
prediction = model.predict(x_test)
prediction = np.around(prediction)
y_test_non_category = [ np.argmax(t) for t in y_test ]
y_predict_non_category = [ np.argmax(t) for t in prediction ]
from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test_non_category, y_predict_non_category)
print (conf_mat)
I hope I can get some advice, thanksss.
The x_train example
x_train
y_train before converted to categorical
enter image description here
Your final Dense layer has 4 outputs, it seems like you are classifying 4 instead of 3.
model.add(Dense(3, activation='softmax')) # Number of classes 3
It would be helpful to see sample data from x_train and y_train to make sure the pre-processing is correct. Because you have only 1 feature, a MLP might be overkill. A decision tree would be simpler unless you want to experiment with MLPs.
I am new to Keras and RNN
I need to build a Classifier Model using LSTM RNN in Keras for a Dataset that contain a train set of shape (1795575, 6) and labels array of shape (1795575, 1).The labels is 11 class (from 0 to 10)
The test set of shape (575643, 6) and Labels array of shape (575643, 1.Again, the labels is 11 (from 0 to 10)
How can I shape the following Keras Model to satisfy my Dataset.What Values should I put for ?
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.optimizers import SGD
import numpy as np
data_dim = ?
timesteps = ?
num_classes = ?
batch_size = ?
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = Sequential()
model.add(LSTM(32, return_sequences=True, stateful=True,batch_input_shape=
(batch_size, timesteps, data_dim)))
model.add(LSTM(32, return_sequences=True, stateful=True))
model.add(LSTM(32, stateful=True))
model.add(Dense(?, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',
metrics=['accuracy'])
model.fit(train_X_arr, train_y_arr,batch_size=batch_size, epochs=epochs,
shuffle=False,validation_data=(test_X_arr, test_y_arr))
I appreciate your help and Thanks in advance
What you would like to do is this:
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.optimizers import SGD
import numpy as np
data_dim = 1 # EACH TIMESTAMP IS SCALAR SO SHAPE=1
timesteps = 6 # EACH EXAMPLE CONTAINS 6 TIMESTAMPS
num_classes = 1 # EACH LABEL IS ONE NUMBER SO SHAPE=1
batch_size = 1 # TAKE SIZE THAT CAN DIVIDE THE NUMBER OF EXAMPLES IN THE TRAIN DATA. THE HIGHER THE BATCH SIZE THE BETTER!
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = Sequential()
model.add(LSTM(32, return_sequences=True, stateful=True,batch_input_shape=
(batch_size, timesteps, data_dim)))
model.add(LSTM(32, return_sequences=True, stateful=True))
model.add(LSTM(32, stateful=True))
model.add(Dense(1, activation='softmax')) # AT THE END YOU WANT ONE VALUE (LIKE THE LABELS) -> SO DENSE SHOULD OUTPUT 1 NODE
model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',
metrics=['accuracy'])
model.fit(train_X_arr, train_y_arr,batch_size=batch_size, epochs=epochs,
shuffle=False,validation_data=(test_X_arr, test_y_arr))
and that's it.
EDIT: In addition, make sure that you reshape your train data to be: (1795575, 6,1) -> 1795575 examples, each has 6 timestamps, each timestamps is scalar.
You can achieve that easily by using np.expand_dims(train_data,-1).
I am running keras over tensorflow, trying to implement a multi-dimensional LSTM network to predict a linear continuous target variable , a single value for each example(return_sequences = False).
My sequence length is 10 and number of features (dim) is 11.
This is what I run:
import pprint, pickle
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
# Input sequence
wholeSequence = [[0,0,0,0,0,0,0,0,0,2,1],
[0,0,0,0,0,0,0,0,2,1,0],
[0,0,0,0,0,0,0,2,1,0,0],
[0,0,0,0,0,0,2,1,0,0,0],
[0,0,0,0,0,2,1,0,0,0,0],
[0,0,0,0,2,1,0,0,0,0,0],
[0,0,0,2,1,0,0,0,0,0,0],
[0,0,2,1,0,0,0,0,0,0,0],
[0,2,1,0,0,0,0,0,0,0,0],
[2,1,0,0,0,0,0,0,0,0,0]]
# Preprocess Data:
wholeSequence = np.array(wholeSequence, dtype=float) # Convert to NP array.
data = wholeSequence
target = np.array([20])
# Reshape training data for Keras LSTM model
data = data.reshape(1, 10, 11)
target = target.reshape(1, 1, 1)
# Build Model
model = Sequential()
model.add(LSTM(11, input_shape=(10, 11), unroll=True, return_sequences=False))
model.add(Dense(11))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(data, target, nb_epoch=1, batch_size=1, verbose=2)
and get the error ValueError: Error when checking target: expected activation_1 to have 2 dimensions, but got array with shape (1, 1, 1)
Not sure what should the activation layer should get (shape wise)
Any help appreciated
thanks
If you just want to have a single linear output neuron, you can simply use a dense layer with one hidden unit and supply the activation there. Your output then can be a single vector without the reshape- I adjusted your given example code to make it work:
wholeSequence = np.array(wholeSequence, dtype=float) # Convert to NP array.
data = wholeSequence
target = np.array([20])
# Reshape training data for Keras LSTM model
data = data.reshape(1, 10, 11)
# Build Model
model = Sequential()
model.add(LSTM(11, input_shape=(10, 11), unroll=True, return_sequences=False))
model.add(Dense(1, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(data, target, nb_epoch=1, batch_size=1, verbose=2)