Basically, I am taking two images as inputs and preprocessing them and passing them as input to the Siamese CNN model.
def create_base_network_signet(input_shape):
seq = Sequential()
seq.add(Conv2D(96, kernel_size=(11, 11), activation='relu', name='conv1_1', strides=4, input_shape= input_shape,
kernel_initializer='glorot_uniform'))
seq.add(BatchNormalization(epsilon=1e-06, axis=1, momentum=0.9))
seq.add(MaxPooling2D((3,3), strides=(2, 2)))
seq.add(ZeroPadding2D((2, 2)))
seq.add(Conv2D(256, kernel_size=(5, 5), activation='relu', name='conv2_1', strides=1, kernel_initializer='glorot_uniform'))
seq.add(BatchNormalization(epsilon=1e-06, axis=1, momentum=0.9))
seq.add(MaxPooling2D((3,3), strides=(2, 2)))
seq.add(Dropout(0.3))
seq.add(ZeroPadding2D((1, 1)))
seq.add(Conv2D(384, kernel_size=(3, 3), activation='relu', name='conv3_1', strides=1, kernel_initializer='glorot_uniform'))
seq.add(ZeroPadding2D((1, 1)))
seq.add(Conv2D(256, kernel_size=(3, 3), activation='relu', name='conv3_2', strides=1, kernel_initializer='glorot_uniform'))
seq.add(MaxPooling2D((3,3), strides=(2, 2)))
seq.add(Dropout(0.3))# added extra
seq.add(Flatten(name='flatten'))
seq.add(Dense(1024, kernel_regularizer=l2(0.0005), activation='relu', kernel_initializer='glorot_uniform'))
seq.add(Dropout(0.5))
seq.add(Dense(128, kernel_regularizer=l2(0.0005), activation='relu', kernel_initializer='glorot_uniform'))
seq.add(Dense(1, activation='sigmoid'))
return seq
My aim to pass images is something similar to this below
result = model.fit([image1, image2], y = 1, epochs=10)
However, I am getting an error
Failed to find data adapter that can handle input: (<class 'list'> containing values of types {"<class 'numpy.ndarray'>"}), <class 'int'>
Related
I am trying to solve a use case of handwritten text recognition. I have used CNN and LSTM to create a network. The output of this needs to be fed to a CTC layer. I could find some codes to do this in native tensorflow. Is there an easier option for this in Keras.
model = Sequential()
model.add(Conv2D(64, kernel_size=(5,5),activation = 'relu', input_shape=(128,32,1), padding='same', data_format='channels_last'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(128, kernel_size=(5,5),activation = 'relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(256, kernel_size=(5,5),activation = 'relu', padding='same'))
model.add(Conv2D(256, kernel_size=(5,5),activation = 'relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(1,2),padding='same'))
model.add(Conv2D(512, kernel_size=(5,5),activation = 'relu', padding='same'))
model.add(BatchNormalization())
model.add(Conv2D(512, kernel_size=(5,5),activation = 'relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), strides=(1,2),padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(1,1)))
model.add(Conv2D(512, kernel_size=(5,5),activation = 'relu', padding='same'))
model.add(Lambda(lambda x: x[:, :, 0, :], output_shape=(None,31,512), mask=None, arguments=None))
#model.add(Bidirectional(LSTM(256, return_sequences=True), input_shape=(31, 256)))
model.add(Bidirectional(LSTM(128, return_sequences=True)))
model.add(Bidirectional(LSTM(128, return_sequences=True)))
model.add(Dense(75, activation = 'softmax'))
Any help on how we can easily add CTC Loss and Decode layers to this would be great
A CTC loss function requires four arguments to compute the loss, predicted outputs, ground truth labels, input sequence length to LSTM and ground truth label length. To get this we need to create a custom loss function and then pass it to the model. To make it compatible with your defined model, we need to create a model which takes these four inputs and outputs the loss. This model will be used for training and for testing, the model that you have created earlier can be used.
Let's create a keras model that you used in a different way so that we can create two different versions of the model to be used at training and testing time.
# input with shape of height=32 and width=128
inputs = Input(shape=(32, 128, 1))
# convolution layer with kernel size (3,3)
conv_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
# poolig layer with kernel size (2,2)
pool_1 = MaxPool2D(pool_size=(2, 2), strides=2)(conv_1)
conv_2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool_1)
pool_2 = MaxPool2D(pool_size=(2, 2), strides=2)(conv_2)
conv_3 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool_2)
conv_4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv_3)
# poolig layer with kernel size (2,1)
pool_4 = MaxPool2D(pool_size=(2, 1))(conv_4)
conv_5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool_4)
# Batch normalization layer
batch_norm_5 = BatchNormalization()(conv_5)
conv_6 = Conv2D(512, (3, 3), activation='relu', padding='same')(batch_norm_5)
batch_norm_6 = BatchNormalization()(conv_6)
pool_6 = MaxPool2D(pool_size=(2, 1))(batch_norm_6)
conv_7 = Conv2D(512, (2, 2), activation='relu')(pool_6)
squeezed = Lambda(lambda x: K.squeeze(x, 1))(conv_7)
# bidirectional LSTM layers with units=128
blstm_1 = Bidirectional(LSTM(128, return_sequences=True, dropout=0.2))(squeezed)
blstm_2 = Bidirectional(LSTM(128, return_sequences=True, dropout=0.2))(blstm_1)
outputs = Dense(len(char_list) + 1, activation='softmax')(blstm_2)
# model to be used at test time
test_model = Model(inputs, outputs)
We will use ctc_loss_fuction during training. So, lets implement the ctc_loss_function and create a training model using ctc_loss_function:
labels = Input(name='the_labels', shape=[max_label_len], dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')
def ctc_lambda_func(args):
y_pred, labels, input_length, label_length = args
return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([outputs, labels,
input_length, label_length])
#model to be used at training time
training_model = Model(inputs=[inputs, labels, input_length, label_length], outputs=loss_out)
--> Train this model and save the weights in .h5 file
Now use the test model and load saved weights of the training model by using arguments by_name=True so it will load weights for only matching layers.
this is my code with error
def createModel():
model = Sequential()
# first set of CONV => RELU => MAX POOL layers
model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=inputShape))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
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(0.25))
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(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))
# returns our fully constructed deep learning + Keras image classifier
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
# use binary_crossentropy if there are two classes
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
return model
ValueError: Error when checking input: expected conv2d_19_input to have 4 dimensions, but got array with shape (274, 1)
I want to apply GridSearchCV on the autoencoder model. The code of the atuoencoder and GridSearchCV is added below please tell me how I change this code to run GridSearchCV successfully.
autoencoder = Sequential()
# Encoder Layers
autoencoder.add(Conv2D(16, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
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), strides=(2,2), activation='relu', padding='same'))
# Flatten encoding for visualization
autoencoder.add(Flatten())
autoencoder.add(Reshape((4, 4, 8)))
# Decoder Layers
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, (3, 3), activation='relu'))
autoencoder.add(UpSampling2D((2, 2)))
autoencoder.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))
autoencoder.summary()
I want to apply GridSearch on the above autoencoder code
from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasClassifier
model_classifier = KerasClassifier(autoencoder, verbose=1, batch_size=10, epochs=10)
# define the grid search parameters
batch_size = [10]
loss = ['mean_squared_error', 'binary_crossentropy']
optimizer = [Adam, SGD, RMSprop]
learning_rate = [0.001]
epochs = [3, 5]
param_grid = dict(optimizer=optimizer, learning_rate=learning_rate)
grid = GridSearchCV(cv=[(slice(None), slice(None))], estimator=model_classifier, param_grid=param_grid, n_jobs=1)
grid_result = grid.fit(x_train, x_train)
print("training Successfully completed")
I have solved this by hard code. I applied for lop on every parameter and get the result.
For best parameter selection I have find the parameter on which I have got high results.
Hello guys I am trying to make pretrained VGG16 on Keras
But it keeps give me error:
ValueError: Error when checking target: expected activation_1 to have
shape (2622,) but got array with shape (1,)
I was trying to create the model based on this poster : Link
Also, I took the pre-trained weight from here. This weight can be read on here
This my code:
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense, ZeroPadding2D
from keras import backend as K
# dimensions of our images.
img_width, img_height = 224, 224
train_data_dir = 'database/train'
validation_data_dir = 'database/validation'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 50
batch_size = 16
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
# build the VGG16 network
model = applications.VGG16(weights='imagenet', include_top=False)
print('VGG Pretrained Model loaded.')
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Conv2D(4096, (7, 7), activation='relu'))
model.add(Dropout(0.5))
model.add(Conv2D(4096, (1, 1), activation='relu'))
model.add(Dropout(0.5))
model.add(Conv2D(2622, (1, 1)))
model.add(Flatten())
model.add(Activation('softmax'))
# model.load_weights('./vgg16_face_weights.h5')
#
# vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 224,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 224)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save_weights('first_try.h5')
You probably have only one folder inside 'database/train' and 'database/validation'.
Please make sure you have 2622 folders in the two folders so that keras can generate the label correctly.
Following is an example showing that the label should have shape of (batch_size, 2622).
# the above remains the same
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
import numpy as np
classes = 2622
batch_size = 4
y = np.zeros((batch_size, classes))
for i in range(batch_size):
y[i, np.random.choice(classes)] = 1
model.fit(x=np.random.random((batch_size,)+input_shape), y=y, batch_size=batch_size)
model.save_weights('first_try.h5')
EDIT:
To change the last Conv2D layer from 2622 filters to 12 filters while maintaining the loaded weights, here is a workaround:
#define model and load_weights
#......
#build a new model based on the last model
conv = Conv2D(12, (1, 1))(model.layers[-4].output)
flatten = Flatten()(conv)
softmax = Activation('softmax')(flatten)
final_model = Model(inputs=model.input, outputs=softmax)
Ref:Cannot add layers to saved Keras Model. 'Model' object has no attribute 'add'
I want to use the Keras Conv2D but got errors:
model.add(Conv2D(64, (2, 2), padding='valid', data_format='channels_last', input_shape=(1, 4, 4, 1)))
The Keras doc tells us that input shape is a 4D tensor, but it throws this error:
ValueError: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=5.
I did some debugging and found there's a check to parameters in topology.py:
if spec.ndim is not None:
if K.ndim(x) != spec.ndim:
raise ValueError('Input ' + str(input_index) +
' is incompatible with layer ' +
self.name + ': expected ndim=' +
str(spec.ndim) + ', found ndim=' +
str(K.ndim(x)))
I found that x = Tensor("conv2d_1_input:0", shape=(?, 1, 4, 4, 1), dtype=float32) is a tensor with dim=5 and spec is an instance of InputSpec with dim=4, it never is equal. How to solve this problem?
The code :
def _build_model(self):
# Neural Net for Deep-Q learning Model
model = Sequential()
model.add(Conv2D(64, (2, 2), padding='valid', data_format='channels_last', input_shape=(1, 4, 4, 1)))
model.add(Conv2D(128, 3, strides=(1, 1), padding='valid'))
model.add(Flatten())
model.add(Dense(16, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
return model
Try this:
model.add(Conv2D(64, (2, 2), padding='valid', data_format='channels_last', input_shape=(4, 4, 1)))
The Convolutional2D layer expects #samples * height * width * channels. The number of samples is inferred from your model.fit() function where you feed in you data.
If you look at MNIST as the simplest example, this works:
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=(28, 28, 1)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
...
history = model.fit(X-train, y_train, batch_size=32, epochs=1)