I'm working on a classification of one dimensional data. I presented the data in the form of 549 arrays, each contains 600 samples. I've made a multi-layer perceptron, which showed an efficiency of about 80%. Now I'm trying to do CNN but for some reason it's accuracy doesn't exceed 31%. What can be wrong?
My model:
model = Sequential()
model.add(Conv1D(filters=20, kernel_size=4,activation='relu',padding='same',input_shape=(600,1)))
model.add(MaxPooling1D(pool_size = 2))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(50, activation='relu', input_dim = 600))
model.add(Dense(1, activation='softmax'))
model.compile(loss="binary_crossentropy", optimizer="nadam", metrics=['accuracy'])
model.fit(np.array(X), np.array(Y), epochs = 100, batch_size=8, verbose=1, validation_data=(np.array(X1),np.array(Y1)))
scores = model.evaluate(np.array(X1), np.array(Y1), verbose=0)
Input data:
X1 = X[:90]
X = X[91:]
Y1 = Y[:90]
Y = Y[91:]
X = np.expand_dims(X, axis=2)
X1 =np.expand_dims(X1, axis=2)
print(np.array(X).shape)
Get the dimension (458, 600, 1) can there be something wrong with the dimensions?
change
model.add(Dense(1, activation='softmax'))
to
model.add(Dense(1, activation='sigmoid'))
Related
This question is in line with the question posted here but with a slight nuance of the CNN. Using the feature extraction definition:
max_pad_len = 174
n_mels = 128
def extract_features(file_name):
try:
audio, sample_rate = librosa.core.load(file_name, res_type='kaiser_fast')
mely = librosa.feature.melspectrogram(y=audio, sr=sample_rate, n_mels=n_mels)
#pad_width = max_pad_len - mely.shape[1]
#mely = np.pad(mely, pad_width=((0, 0), (0, pad_width)), mode='constant')
except Exception as e:
print("Error encountered while parsing file: ", file_name)
return None
return mely
How do you go about getting the correct dimension of the num_rows, num_columns and num_channels to be input to the train and test data?
In constructing the CNN Model, how to determine the correct shape to input?
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=2, input_shape=(num_rows, num_columns, num_channels), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
I dont know if it is exactly your problem but I also have to use a MEL as an input to a CNN.
Short answer:
input_shape = (x_train.shape[1], x_train.shape[2], 1)
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)
or
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)
input_shape = x_train.shape[1:]
Long answer
In my case I have a DataFrame with speakers_id and mel spectrograms (previously calculated with librosa).
The Keras CNN models are prepared for images with width, height and channels of colors (grayscale - RGB)
The Mel Spectrograms given by librosa are image-like arrays with width and height, so you need to do a reshape to add the channel dimension.
Define the input and expected output
# It looks stupid but that way i could convert the panda.Series to a np.array
x = np.array(list(df.mel))
y = df.speaker_id
print('X shape:', x.shape)
X shape: (2204, 128, 24)
2204 Mels, 128x24
Split in train-test
x_train, x_test, y_train, y_test = train_test_split(x, y)
print(f'Train: {len(x_train)}', f'Test: {len(x_test)}')
Train: 1653 Test: 551
Reshape to add the extra dimension
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1)
print('Shapes:', x_train.shape, x_test.shape)
Shapes: (1653, 128, 24, 1) (551, 128, 24, 1)
Set input_shape
# The input shape is independent of the amount of inputs
input_shape = x_train.shape[1:]
print('Input shape:', input_shape)
Input shape: (128, 24, 1)
Put it into the model
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D())
# More layers...
model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['accuracy'])
Run model
model.fit(x_train, y_train, epochs=20, validation_data=(x_test, y_test))
Hope this is helpfull
X and Y is of shape (89362, 5) and (89362,) repectively.
x_train, x_test, y_train, y_test = train_test_split(X, Y,
test_size = 0.3,
random_state = 1)
x_train.shape, y_train.shape = ((62553, 5), (62553,))
x_test.shape, y_test.shape = ((26809, 5), (26809,))
Reshaped the vectors to:
torch.Size([1, 62553, 5]), torch.Size([1, 62553])
torch.Size([1, 26809, 5]), torch.Size([1, 26809])
The model is defined as
n_steps = 62553
n_features = 5
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features)))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(50, activation='relu'))
model.add(Dense(62553))
model.compile(optimizer='adam', loss='mse')
model.fit(x_train, y_train, epochs=10, verbose=0)
While predicting with x_test, it throws value error
yhat = model.predict(x_test, verbose=0)
print(yhat)
ValueError: Error when checking input: expected conv1d_4_input to have shape (62553, 5) but got array with shape torch.Size([26809, 5])
This is happening because you are specifying a fixed size here:
model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features)))
Once you pass something else to the model, the model is still expecting that fixed size with dimensions:
n_steps = 62553
n_features = 5
Removing the input_shape parameter should correct this issue:
model.add(Conv1D(filters=64, kernel_size=2, activation='relu'))
I hope that this helps you.
I have a pandas dataframe containing filenames of positive and negative examples as below
img1 img2 y
001.jpg 002.jpg 1
003.jpg 004.jpg 0
003.jpg 002.jpg 1
I want to train my Siamese network using Keras ImageDataGenerator and flow_from_dataframe. How do I set up my training so that the code inputs 2 images with 1 label simultaneously.
Below is the code for my model
def siamese_model(input_shape) :
left = Input(input_shape)
right = Input(input_shape)
model = Sequential()
model.add(Conv2D(32, (3,3), activation='relu', input_shape=input_shape))
model.add(BatchNormalization())
model.add(Conv2D(64, (3,3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(128, (3,3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(256, (3,3), activation='relu')
model.add(BatchNormalization())
model.add(Conv2D(256, (3,3), activation='relu')
model.add(MaxPooling2D())
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(512, activation='sigmoid'))
left_encoded = model(left)
right_encoded = model(right)
L1_layer = Lambda(lambda tensors:K.abs(tensors[0] - tensors[1]))
L1_distance = L1_layer([left_encoded, right_encoded])
prediction = Dense(1,activation='sigmoid')(L1_distance)
siamese_net = Model(inputs=[left,right],outputs=prediction)
return siamese_net
model = siamese_model((224,224,3))
model.compile(loss="binary_crossentropy",optimizer="adam", metrics=['accuracy'])
datagen_left = ImageDataGenerator(rotation_range=10,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
vertical_flip = True)
datagen_right = ImageDataGenerator(rotation_range=10,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
vertical_flip = True)
Join the generators in a custom generator.
Make one of them output the desired labels, discard the label of the other.
class DoubleGenerator(Sequence):
def __init__(self, gen1, gen2):
self.gen1 = gen1
self.gen2 = gen2
def __len__(self):
return len(self.gen1)
def __getitem__(self, i):
x1,y = self.gen1[i]
x2,y2 = self.gen2[i]
return (x1,x2), y
Use it:
double_gen = DoubleGenerator(datagen_left.flow_from_directory(...),
datagen_right.flow_from_directory(...))
I have xtrain.shape as
(60000, 28, 28)
It means 60000 channels with image size 28 * 28
I want to make a keras Sequential model.
specifying the model shape
model = Sequential()
model.add(Convolution2D(32,3,activation='relu',input_shape=(????)))
model.add(Dense(10, activation='relu'))
model.summary()
what input_shape should looks like?
model = Sequential()
model.add(Dense(64,input_shape=(1,28,28)))
when I put this I got an following error
Error when checking input: expected dense_31_input to have 4 dimensions, but got array with shape (60000, 28, 28)
why this required 4 dimensions? and how to fix it form code?
I have xtrain.shape as
(60000, 28, 28)
It means 60000 channels with image size 28 * 28
Well, it certainly does not mean that; it means 60000 samples, not channels (MNIST is a single-channel dataset).
No need to re-invent the wheel in such cases - have a look at the MNIST CNN example in Keras:
from keras import backend as K
# input image dimensions
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first': # Theano backend
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else: # Tensorflow backend
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
# normalise:
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# your model:
model = Sequential()
model.add(Convolution2D(32,3,activation='relu',input_shape=input_shape))
model.add(Dense(10, activation='softmax')) # change to softmax in the final layer
where you should also change the activation of the final layer to softmax (and most probably add some pooling and flatten layers before the final dense one).
Try to reshape data to (60000, 28, 28, 1) or (60000, 1, 28, 28).
First one,
model = Sequential()
model.add(Convolution2D(32,3,activation='relu',input_shape=(60000,28,28)))
model.add(Dense(10, activation='relu'))
model.summary()
Second one,
model = Sequential()
model.add(Dense(64,input_shape=(None,60000,28,28)))
I am trying to feed 1-D signal(1,2000) which has 22 features(22,2000) into LSTM.
(1-D signal is taken by 10 seconds with 200 hz sampling rate)
And I have 808 batches. (808, 22, 2000)
I saw that the LSTM receives 3D tensor shape of (batch_size, timestep, input_dim).
So is it right that my input shape as?
: (batch_size = 808, timestep = 2000, input_dim = 3)
here is my sample of code.
# data shape check
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
(727, 22, 2000)
(81, 22, 2000)
(727, 2)
(81, 2)
# Model Config
inputshape = (808,2000,2) # 22 chanel, 2000 samples
lstm_1_cell_num = 20
lstm_2_cell_num = 20
inputdrop_ratio = 0.2
celldrop_ratio = 0.2
# define model
model = Sequential()
model.add(LSTM(lstm_1_cell_num, input_shape=inputshape, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(20))
model.add(LSTM(lstm_2_cell_num, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(2, activation='sigmoid'))
print(model.summary())
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
First input shape must be (22,2000) and batch size should be given in the fit function. So try this
inputshape = (22,2000)
model.fit(X_train, y_train,
batch_size=808,
epochs=epochs,
validation_data=(X_test,y_test),
shuffle=True)