I work on image classification with 10 classes.
each image is represented as a set of sequences (=75 sequence). Each sequence is represented as a set (=42 visual words) of visuel words. each word is encoded accoardind to a visual vocabulary (size=200)
so each image is represented as a tensor of shape (75, 42,200).
I want to use LSTM network to model this image representation using this code
model = Sequential()
model.add(LSTM(128, activation='relu', input_shape=(75, 42,200))))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
i get this error message:
ValueError Traceback (most recent call last)
<ipython-input-30-da9ec53d6d59> in <module>
1 model = Sequential()
----> 2 model.add(LSTM(128, activation='relu', input_shape=(75,42,200))) #number_of_hidden_units=128
3 model.add(Dense(10, activation='softmax')) #since number of output classes is 10
4 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
5 model.summary()
2 frames
/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
212 ndim = shape.rank
213 if ndim != spec.ndim:
--> 214 raise ValueError(f'Input {input_index} of layer "{layer_name}" '
215 'is incompatible with the layer: '
216 f'expected ndim={spec.ndim}, found ndim={ndim}. '
ValueError: Input 0 of layer "lstm_1" is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: (None, 75, 42, 200)
What is wrong. Please help
thank you
perform LSTM model with more than 3D image representation
Related
enter image description here
Code:
resnet_model = tf.keras.models.Sequential()
pretrained_model= tf.keras.applications.ResNet50(include_top=False,
input_shape=(48,48,1),
pooling='avg',classes=7,
weights='imagenet')
for layer in pretrained_model.layers:
layer.trainable=False
resnet_model.add(pretrained_model)
resnet_model.add(tf.keras.layers.Flatten())
resnet_model.add(tf.keras.layers.Dense(512, activation='relu'))
resnet_model.add(tf.keras.layers.Dense(5, activation='softmax'))
I want to use Resnet50 for 1 channel but I am getting the error. Is there any solution to this problem?
I am very new in this field. I searched on the internet but I could not find a solution. I am waiting for the help of people who are interested in this field.
My model
def load_VGG16_model():
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(256,256,3))
print("Model loaded..!")
return base_model
Summary of the model
load_VGG16_model().summary()
Adding Layers
def action_model(shape=(30, 256, 256, 3), nbout=len(classes)):
convnet = load_VGG16_model()
model = Sequential()
model.add(TimeDistributed(convnet, input_shape=shape))
model.add(LSTM(30,return_sequences=True,input_shape=(30,512))) # the error shows this line.
top_model.add(Dense(4096, activation='relu', W_regularizer=l2(0.1)))
top_model.add(Dropout(0.5))
top_model.add(Dense(4096, activation='relu', W_regularizer=l2(0.1)))
top_model.add(Dropout(0.5))
model.add(Dense(nbout, activation='softmax'))
return model
model.add(LSTM(30,return_sequences=True,input_shape=(30,512))) ==> the error shows this line.
Your problem is similar to this one Building CNN + LSTM in Keras for a regression problem. What are proper shapes?
Using reshape layer before the LSTM should work fine for you
def action_model(shape=(256, 256, 3), nbout=len(classes)):
convnet = load_VGG16_model()
model = Sequential()
model.add(convnet)
model.add(tf.keras.layers.Reshape((8*8, 512))) # Shape comes from the last output of covnet
model.add(LSTM(30,return_sequences=True,input_shape=(8*8,512))) # the error shows this line.
top_model.add(Dense(4096, activation='relu', W_regularizer=l2(0.1)))
top_model.add(Dropout(0.5))
top_model.add(Dense(4096, activation='relu', W_regularizer=l2(0.1)))
top_model.add(Dropout(0.5))
model.add(Dense(nbout, activation='softmax'))
return model
I have the following (2D) convolutional neural network in Keras for image classification with binary labels:
model = keras.Sequential()
model.add(Conv2D(32, kernel_size=5, activation='relu', input_shape=(128, 128, 1)))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(64, kernel_size=5, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Flatten())
model.add(Dense(1024, activation="relu"))
model.add(Dense(2, activation="softmax"))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
To train it, I have a lot of (.jpeg) image files, but too many to load all at once. Hence I use the following generator (and preprocessing):
def load_preprocess(path):
img = img_to_array(load_img(path, target_size=(128, 128)))
output = rgb_to_grayscale(img)
output = tf.reshape(output, (-1,128, 128, 1))
return output
def image_generator(paths, labels, batch_size=32):
while True:
for i in range(0, len(paths), batch_size):
images = [load_preprocess(path) for path in paths[i:i+batch_size]]
target = labels[i:i+batch_size]
yield(images, target)
I tried training the network using
model.fit_generator(image_generator(train_paths, train_labels), steps_per_epoch=int(np.ceil(len(train_paths)/32)), epochs=1)
Here train_paths is a list of paths and train_labels is a binary numpy array with two columns.
However, this gives me the following error:
InvalidArgumentError: Requested tensor connection from unknown node: "conv2d_input:0".
What causes this error and how do I solve it? I tried googling it, but I found no hits at all.
I found the error: the images are tensors and should be converted to arrays. I do this as follows:
def image_generator(paths, labels, batch_size=32):
sess = tf.Session()
while True:
for i in range(0, len(paths), batch_size):
with sess.as_default():
images = [load_preprocess(path).eval() for path in paths[i:i+batch_size]]
target = labels[i:i+batch_size]
yield(np.array(images), target)
I built my keras model in the following way (this is of course not the final production ready model):
self.model = Sequential()
self.model.add(Conv2D(32, (3, 3), input_shape=(674, 514, 1), padding='same',
activation='relu'))
self.model.compile(loss='mean_squared_error', optimizer='adam', metrics=
['accuracy'])
Model summary is:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 674, 514, 32) 320
=================================================================
Total params: 320
Trainable params: 320
Non-trainable params: 0
_________________________________________________________________
I try to fit it in the following way:
self.model.fit(self.input_images, self.output_images, batch_size=32,
epochs=10, verbose=1, shuffle=True)
Shapes of both training input and output (self.input_images, self.output_images) are both (100, 674, 514, 1).
And when i try to train my model I get the following exception:
ValueError: Error when checking target: expected conv2d_1 to have shape
(674, 514, 32) but got array with shape (674, 514, 1)
Any help is much appreciated.
The mismatch is with your output_images. The result of the convolutional layer is (None, 674, 514, 32), because it has 32 filters. The loss mean_squared_error tells keras to expect a compatible label shape (which the supplied output_images is not).
The model isn't finished and normally CNN has many convolutional and downsampling layers, so the output shape is going to be different. But if you want you can make this model work by either changing the number of filters to 1...
Conv2D(1, ...)
... or by making output_images a tensor of shape (100, 674, 514, 32).
So I'm trying to practice how to use LSTMs in Keras and all parameter (samples, timesteps, features). 3D list is confusing me.
So I have some stock data and if the next item in the list is above the threshold of 5 which is +-2.50 it buys OR sells, if it is in the middle of that threshold it holds, these are my labels: my Y.
For my features my X I have a dataframe of [500, 1, 3] for my 500 samples and each timestep is 1 since each data is 1 hour increment and 3 for 3 features. But I get this error:
ValueError: Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (500, 3)
How can I fix this code and what am I doing wrong?
import json
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
"""
Sample of JSON file
{"time":"2017-01-02T01:56:14.000Z","usd":8.14},
{"time":"2017-01-02T02:56:14.000Z","usd":8.16},
{"time":"2017-01-02T03:56:15.000Z","usd":8.14},
{"time":"2017-01-02T04:56:16.000Z","usd":8.15}
"""
file = open("E.json", "r", encoding="utf8")
file = json.load(file)
"""
If the price jump of the next item is > or < +-2.50 the append 'Buy or 'Sell'
If its in the range of +- 2.50 then append 'Hold'
This si my classifier labels
"""
data = []
for row in range(len(file['data'])):
row2 = row + 1
if row2 == len(file['data']):
break
else:
difference = file['data'][row]['usd'] - file['data'][row2]['usd']
if difference > 2.50:
data.append((file['data'][row]['usd'], 'SELL'))
elif difference < -2.50:
data.append((file['data'][row]['usd'], 'BUY'))
else:
data.append((file['data'][row]['usd'], 'HOLD'))
"""
add the price the time step which si 1 and the features which is 3
"""
frame = pd.DataFrame(data)
features = pd.DataFrame()
# train LSTM
for x in range(500):
series = pd.Series(data=[500, 1, frame.iloc[x][0]])
features = features.append(series, ignore_index=True)
labels = frame.iloc[16000:16500][1]
# test
#yt = frame.iloc[16500:16512][0]
#xt = pd.get_dummies(frame.iloc[16500:16512][1])
# create LSTM
model = Sequential()
model.add(LSTM(3, input_shape=features.shape, activation='relu', return_sequences=False))
model.add(Dense(2, activation='relu'))
model.add(Dense(1, activation='relu'))
model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
model.fit(x=features.as_matrix(), y=labels.as_matrix())
"""
ERROR
Anaconda3\envs\Final\python.exe C:/Users/Def/PycharmProjects/Ether/Main.py
Using Theano backend.
Traceback (most recent call last):
File "C:/Users/Def/PycharmProjects/Ether/Main.py", line 62, in <module>
model.fit(x=features.as_matrix(), y=labels.as_matrix())
File "\Anaconda3\envs\Final\lib\site-packages\keras\models.py", line 845, in fit
initial_epoch=initial_epoch)
File "\Anaconda3\envs\Final\lib\site-packages\keras\engine\training.py", line 1405, in fit
batch_size=batch_size)
File "\Anaconda3\envs\Final\lib\site-packages\keras\engine\training.py", line 1295, in _standardize_user_data
exception_prefix='model input')
File "\Anaconda3\envs\Final\lib\site-packages\keras\engine\training.py", line 121, in _standardize_input_data
str(array.shape))
ValueError: Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (500, 3)
"""
Thanks.
This is my first post here I wish that could be useful I will try to do my best
First you need to create 3 dimension array to work with input_shape in keras you can watch this in keras documentation or in a better way:
from keras.models import Sequential
Sequential?
Linear stack of layers.
Arguments
layers: list of layers to add to the model.
# Note
The first layer passed to a Sequential model
should have a defined input shape. What that
means is that it should have received an input_shape
or batch_input_shape argument,
or for some type of layers (recurrent, Dense...)
an input_dim argument.
Example
```python
model = Sequential()
# first layer must have a defined input shape
model.add(Dense(32, input_dim=500))
# afterwards, Keras does automatic shape inference
model.add(Dense(32))
# also possible (equivalent to the above):
model = Sequential()
model.add(Dense(32, input_shape=(500,)))
model.add(Dense(32))
# also possible (equivalent to the above):
model = Sequential()
# here the batch dimension is None,
# which means any batch size will be accepted by the model.
model.add(Dense(32, batch_input_shape=(None, 500)))
model.add(Dense(32))
After that how to transform arrays 2 dimensions in 3 dimmension
check np.newaxis
Useful commands that help you more than you expect:
Sequential?,
-Sequential??,
-print(list(dir(Sequential)))
Best