I want to create VGG model with Keras.
However, following error was displayed:
expected lstm_input_2 to have 4 dimensions, but got array with shape
(60000, 10)
I created the following sequential model:
model = Sequential()
model.add(Conv2D(16, kernel_size=(3, 3),
padding='same',
input_shape=input_shape))
model.add(Activation('relu'))
model.add(Conv2D(16, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dense(50, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Dropout(0.5))
model.add(Activation('softmax'))
Please tell me why this error created.
You just need to add a Flatten layer like so:
…
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) # <-- this layer is missing in your code
model.add(Dense(50, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Dropout(0.5))
model.add(Activation('softmax'))
…
This transforms your last 2d layer (MaxPooling2D) to a 1-dimensional shape that you than can feed into your Dense layer.
Related
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
As far as I understand, model.add(Conv2D(32, (3, 3), input_shape=input_shape)) is the input layer here and model.add(Activation('sigmoid')) is the output layer.
There are a total 13 other layers between the input and output layers. So are there 13 hidden layers in the model? Or less? What are the names of the layers that should be counted as hidden layers?
I am confused about whether Activation or MaxPooling2D or Dropout should be counted as a single hidden layer or not.
Activation functions are not the hidden layers.
Layers will be - Conv2D,MaxPooling2D,Flatten,Dense
You can use below code to get the model architecture details.
model.summary()
I am trying to make an emotion classifier using face expressions with FER2013 dataset. It contains of 35887 samples with 2304 features each and an integer label 0-6 for emotions. When I was using Conv1D with shape (2304,1) then it achieved training accuracy of ~86% but wasn't doing well on any unseen test image. So I thought of reshaping it to (48,48,1) for each sample and using Conv2D on it. But now it just gets stuck on 0.2505 while training after the 2nd epoch and never increases. What's happening?
import pandas as pd
import numpy as np
from PIL import Image
import matplotlib.image as mpimg
from skimage import transform
import random
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
emotion = {0 : 'Angry', 1 : 'Disgust',2 : 'Fear',3 : 'Happy',
4 : 'Sad',5 : 'Surprise',6 : 'Neutral'}
df=pd.read_csv('fer.csv')
faces=df.values[:,1]
faces=faces.tolist()
emos=df.values[:,0]
for i in range(len(faces)):
faces[i]=[int(x) for x in faces[i].split()]
emos[i]=int(emos[i])
faces=np.array(faces)
faces=transform.resize(faces, (35887,48,48))
faces=np.expand_dims(faces, axis=3)
model = Sequential()
model.add(Conv2D(48, (3,3), padding='same', input_shape=(48,48,1), activation='relu'))
model.add(Conv2D(48, (3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(96, (3,3), padding='same', activation='relu'))
model.add(Conv2D(96, (3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(192, (3,3), padding='same', activation='relu'))
model.add(Conv2D(192, (3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(384, (3,3), padding='same', activation='relu'))
model.add(Conv2D(384, (3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(384, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(192, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(96, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(faces,emos,epochs=100,batch_size=48)
model.save_weights('model.h5')
Model Accuracy Curves
Model Loss Curves
Normalizing the output batch after each layer fixes the issue.
Just add
model.add(BatchNormalization())
after every layer.
EDIT :
Thought that I should add more information here.
So, this was the final model I ended up making.
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(200,200,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(256, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(len(classes), activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='nadam',
metrics=['accuracy'])
And these were the results I got with it.
I increased the nodes and changed the optimizer too but it was the batch normalisation which gave a dramatic increase in accuracy. Using more nodes and nadam optimizer further helped a bit.
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(3, 150, 150),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature
vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
#sgd = optimizers.SGD(lr=0.0001, decay=1e-6, momentum=0.9)
model.compile(loss='sparse_categorical_crossentropy',
`optimizer=Adam(lr=0.001), # Adam optimizer with 1.0e-4 learning rate
metrics = ['accuracy']) # Metrics to be evaluated by the model
When I compile the above code i get this error
Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_8/MaxPool' (op: 'MaxPool') with input shapes: [?,1,75,32].
I tried with the same padding and it still doesn't work
Pretty sure if you change
model.add(Conv2D(32, (3, 3), input_shape=(3, 150, 150),padding='same'))
to
model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3),padding='same'))
(you may have to change the shape of your data too)
it will work as intended.
I am facing a problem using a convolutional neural network using Keras with Tensorflow as backend with Anaconda Python.
While defining my CNN and compiling, an error occurs:
def cnn_model():
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',
input_shape=(3, 48, 48),
activation='relu'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(64, (3, 3), padding='same',
activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(128, (3, 3), padding='same',
activation='relu'))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(NUM_CLASSES, activation='softmax'))
return model
The error that I get is:
File
"C:\Users\pandey\Anaconda3\lib\site-packages\keras\engine\training.py",
line 113, in _standardize_input_data
'with shape ' + str(data_shape))
ValueError: Error when checking input: expected conv2d_10_input to
have 4 dimensions, but got array with shape (0, 1)
I am using channel first in Keras and have defined the data format as channel first in starting only.
Any help is appreciated.
I've used Floyd hub to to train the following model and saved it
# Create the model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(3, 32, 32), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
epochs = 50
adammax = keras.optimizers.Adamax(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy', optimizer=adammax, metrics=['accuracy'])
print(model.summary())
When I try to load it on my PC, it's working fine. But when I load it on the Raspberry Pi I get the following error. I tried also to save just the weights and load them, but it didn't work and I got the same error. I am using the same version of Tensorflow as Floyd hub on the Raspberry Pi.
As mentioned above, you're passing T=DT_INT64, while that is not one of the supported kernels for this op. You could see if the int64 version is just not shipped in the .so file, write the op kernel yourself, or try casting to tf.int32 right before this op in the python code. The last one worked well for me.