I have a simple convolutional autoencoder in keras. My original inputs are flat arrays from a csv, so I want to reshape them from (196,) to (14,14,1). Following the keras docs, I did:
autoencoder = Sequential()
# first, reshape our (csv) inputs from (196,) to (14,14,1)
autoencoder.add(Reshape((14,14,1), input_shape=(196,)))
# encoding stage
autoencoder.add(Conv2D(16, (3,3), activation='relu', padding='same'))
autoencoder.add(MaxPooling2D((2, 2), padding='same'))
autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
autoencoder.add(MaxPooling2D((2, 2), padding='same'))
autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
autoencoder.add(MaxPooling2D((2, 2), padding='same'))
# decoding stage
autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
autoencoder.add(UpSampling2D((2, 2)))
autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
autoencoder.add(UpSampling2D((2, 2)))
autoencoder.add(Conv2D(16, (2, 2), activation='relu'))
autoencoder.add(UpSampling2D((2, 2)))
autoencoder.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))
optimizer = optimizers.Adagrad(lr=0.01, epsilon=None, decay=0.001)
autoencoder.compile(optimizer=optimizer, loss='binary_crossentropy')
But I get the error: ValueError: Error when checking target: expected conv2d_35 to have 4 dimensions, but got array with shape (2870, 196)
So it seems to be ignoring the Reshape entirely. Am I making some obvious mistake?
The problem is not the input but the target so the y values you give are not reshaped. The final output of your network is a 4D tensor where you give instead (2870, 196).
Double check the your target array (y values) that you pass onto fit function.
Related
Hello I have created a MobileNetV2 model i want to add layers onto it.
However, i kept getting this error:
"One of the dimensions in the output is <= 0 due to downsampling in conv2d_22. Consider increasing the input size. Received input shape [None, 3, 3, 1280] which would produce output shape with a zero or negative value in a dimension."
this is my code so far:
base_model = tf.keras.applications.MobileNetV2(input_shape=(96,96,3),
include_top=False,
weights='imagenet')
model = tf.keras.Sequential()
model.add(base_model)
model.add(layers.Conv2D(60, kernel_size=(5, 5), strides=(1, 1),
activation='relu',
input_shape=(96,96,3))) #error was highlighted on this line
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(layers.Conv2D(64, kernel_size=(5, 5), strides=(1, 1), activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(500, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
i have tried changing the channel first and last ((96,96,3) and (3,96,96)) on the line that has problem and both produced the same error error.
Appreciate anyone who can help with this thanks.
You cannot downsample a tensor (None, 3, 3, 1280) with a kernel size of (5, 5). Think about what a 2D convolutional layer is actually doing. You could try first flattening the output of the base model and then reshaping the output with the dense and reshape layers:
import tensorflow as tf
base_model = tf.keras.applications.MobileNetV2(input_shape=(96,96,3),
include_top=False,
weights='imagenet')
model = tf.keras.Sequential()
model.add(base_model)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64 * 64 * 3)) # just an example --> choose what you want
model.add(tf.keras.layers.Reshape((64, 64, 3)))
model.add(tf.keras.layers.Conv2D(60, kernel_size=(5, 5), strides=(1, 1),
activation='relu',
input_shape=(96,96,3))) #error was highlighted on this line
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(tf.keras.layers.Conv2D(64, kernel_size=(5, 5), strides=(1, 1), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(500, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
You also use an Upsampling layer to get the dimensions you want, but that is up to you.
I have pretrained weights as np.array of shape (3, 3, 3, 64). I want to initialize this Tensorflow CNN with those weights using set_weights() like I show below.
However, when I try that, the following error pops up: ValueError: You called set_weights(weights) on layer "conv2d_3" with a weight list of length 3, but the layer was expecting 2 weights. Provided weights: [[[[-0.15836713 -0.178757 0.16782044 ...
model = models.Sequential()
model.add(layers.Conv2D(64, (3, 3), activation='relu', input_shape=(224, 224, 3)))
model.layers[0].set_weights(weights)
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(256, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(256, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(512, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(512, (3, 3), activation='relu'))
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dense(4, activation='softmax'))
print(model.summary())
adam = optimizers.Adam(learning_rate=0.0001, amsgrad=False)
model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=['accuracy'])
history = model.fit_generator(
train_generator,
steps_per_epoch=np.ceil(nb_train_samples/batch_size),
epochs=epochs,
validation_data=validation_generator,
validation_steps=np.ceil(nb_validation_samples / batch_size),
class_weight=class_weight
)
My question is: how do I pass those (3, 3, 3, 64) shaped weights to initialize that CNN? I have already checked the weight shapes required for each layer and the shapes I am trying to pass and the required shape match.
You could just use kernel_initializer and bias_initializer arguments like this:
import numpy as np
# init_kernel and init_bias are initialization weights that you have
init_kernel = np.random.normal(0, 1, (3, 3, 3, 64))
init_bias = np.zeros((64,))
kernel_initializer = tf.keras.initializers.constant(init_kernel)
bias_initializer = tf.keras.initializers.constant(init_bias)
conv_layer = tf.keras.layers.Conv2D(64, (3, 3),
activation='relu',
input_shape=(224, 224, 3),
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)
Note the kernel's and bias' shapes that I've chosen. The values with which you initialise your layer must have the exact same shapes.
I have train & test image data for which Shapes are given below.
X_test.shape , y_test.shape , X_train.shape , y_train.shape
((277, 128, 128, 3), (277, 1), (1157, 128, 128, 3), (1157, 1))
I am training a model
def baseline_model():
filters = 100
model = Sequential()
model.add(Conv2D(filters, (3, 3), input_shape=(128, 128, 3), padding='same', activation='relu'))
#model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Conv2D(filters, (3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Flatten())
model.add(Conv2D(filters, (3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(Conv2D(filters, (3, 3), activation='relu', padding='same'))
model.add(Activation('linear'))
model.add(BatchNormalization())
model.add(Dense(512, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
lrate = 0.01
epochs = 10
decay = lrate/epochs
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
model.compile(loss='sparse_categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
print(model.summary())
return model
But I am getting an error Given below
Error when checking target: expected dense_35 to have 4 dimensions,
but got array with shape (1157, 1)
Please tell me what mistake I am making and how to fix this. I have attached snapshot of model summary
One thing you have probably forgotten to do is adding a Flatten layer right before the first Dense layer:
model.add(BatchNormalization())
model.add(Flatten()) # flatten the output of previous layer before feeding it to Dense layer
model.add(Dense(512, activation='relu'))
You need it because Dense layer does not flatten its input; rather, it is applied on the last dimension.
Although dense_35 needs to feed with 4 dimension data, according to the error, the network feed with 2 dimension data which is the label vector.
I am new to Keras and the Siamese network architecture. I have developed a Siamese network with three inputs and one output as follows.
def get_siamese_model(input_shape):
# Define the tensors for the three input phrases
anchor = Input(input_shape, name='anchor')
positive = Input(input_shape, name='positive')
negative = Input(input_shape, name='negative')
# Convolutional Neural Network
model = Sequential()
model.add(Conv2D(64, kernel_size=(2, 2), activation='relu', input_shape=input_shape, padding='same'))
model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(Conv2D(16, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(Conv2D(8, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(Conv2D(4, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(Conv2D(2, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(Conv2D(1, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2,1)))
model.add(Flatten())
# Generate the encodings (feature vectors) for the three phrases
anchor_out = model(anchor)
positive_out = model(positive)
negative_out = model(negative)
# Add a customized layer to combine individual output
concat = Lambda(lambda tensors:K.concatenate((tensors[0],tensors[1],tensors[2]),0))
output = concat([anchor_out, positive_out, negative_out])
# Connect the inputs with the outputs
siamese_net = Model(inputs=[anchor,positive,negative],outputs=output)
#plot the model
plot_model(siamese_net, to_file='siamese_net.png',show_shapes=True, show_layer_names=True)
#Error optimization
siamese_net.compile(optimizer=Adam(),
loss=triplet_loss)
# return the model
return siamese_net
while using model.fit() I have written following code:
model = get_siamese_model(input_shape)
X = {
'anchor' : anchor,
'positive' : positive,
'negative' : negative
}
model.fit(np.asarray(X), Y)
I am getting following error message:
ValueError: Error when checking model input:
The list of Numpy arrays that you are passing to your model is not the size the model expected.
Expected to see 3 array(s), but instead got the following list of 1 arrays: [array({'anchor': array([[[[ 4.49218750e-02]...
Any help is appreciated. Thank you in advance.
The following code works for me. Because your names are (anchor, positive, negative), you can use those directly as the keys to your dictionary when passing input. Also, you should make use of the concatenate layer in Keras instead of defining a Lambda. Note that I changed the loss for purposes of this example.
from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, concatenate
from keras.models import Model, Sequential
from keras.optimizers import Adam
from keras.losses import mean_squared_error
import numpy as np
def get_siamese_model(input_shape):
# Define the tensors for the three input phrases
anchor = Input(input_shape, name='anchor')
positive = Input(input_shape, name='positive')
negative = Input(input_shape, name='negative')
# Convolutional Neural Network
model = Sequential()
model.add(Conv2D(64, kernel_size=(2, 2), activation='relu', input_shape=input_shape, padding='same'))
model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(Conv2D(16, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(Conv2D(8, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(Conv2D(4, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(Conv2D(2, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(Conv2D(1, kernel_size=(2, 2), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2,1)))
model.add(Flatten())
# Generate the encodings (feature vectors) for the three phrases
anchor_out = model(anchor)
positive_out = model(positive)
negative_out = model(negative)
# Add a concatenate layer
output = concatenate([anchor_out, positive_out, negative_out])
# Connect the inputs with the outputs
siamese_net = Model(inputs=[anchor,positive,negative],outputs=output)
# Error optimization
siamese_net.compile(optimizer=Adam(), loss=mean_squared_error)
# Summarize model
siamese_net.summary()
# Return the model
return siamese_net
input_shape = (100, 100, 1)
model = get_siamese_model(input_shape)
X = {'anchor': np.ones((5, 100, 100, 1)), # define input as dictionary
'positive': np.ones((5, 100, 100, 1)),
'negative': np.ones((5, 100, 100, 1))}
Y = np.ones((5, 15000))
model.fit(X, Y) # use a dictionary
model.fit([i for i in X.values()], Y) # use a list
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.