Some style transfer examples that use Keras initialize the content, style and combination images and feed them into the first layer of VGG16.
However the Convolution2D objects do not seem to accept the set_input method.
input_tensor = K.concatenate([base_image,
style_reference_image,
combination_image], axis=0)
# build the VGG16 network with 3 images as input
first_layer = Convolution2D(64, 3, 3, activation='relu', name='conv1_1')
first_layer.set_input(input_tensor, shape)
model = Sequential()
model.add(first_layer)
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2', border_mode='same'))
model.add(pooling_func())
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1', border_mode='same'))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2', border_mode='same'))
model.add(pooling_func())
Related
I'm trying to train a network for facial recognition, I can't get past this error. I'm really new to programming in Python and even more so with libraries like Tensorflow.
I keep on getting this error related to input shape. Any help would be highly appreciated. Thanks!
labeled_images = data.map(process_file)
print('labeled images: ', labeled_images)
test_data = labeled_images.take(50)
train_data = labeled_images.skip(50)
print('test data: ', test_data)
print('train data: ', train_data)
this is where the problems start
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(192, 192, 3)))
model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(40))
model.summary()
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy, metrics=['accuracy'])
history = model.fit(train_data, epochs=10, validation_data=test_data)
I printed the "variables"
data: <ZipDataset element_spec=(TensorSpec(shape=(), dtype=tf.string, name=None), TensorSpec(shape=(40,), dtype=tf.int64, name=None))>
labeled images: <MapDataset element_spec=(TensorSpec(shape=(192, 192, 3), dtype=tf.float32, name=None), TensorSpec(shape=(40,), dtype=tf.int64, name=None))>
test data: <TakeDataset element_spec=(TensorSpec(shape=(192, 192, 3), dtype=tf.float32, name=None), TensorSpec(shape=(40,), dtype=tf.int64, name=None))>
train data: <SkipDataset element_spec=(TensorSpec(shape=(192, 192, 3), dtype=tf.float32, name=None), TensorSpec(shape=(40,), dtype=tf.int64, name=None))>
this is the error
ValueError: in user code:
File "C:\Users\Dion\.conda\envs\KerASS\lib\site-packages\keras\engine\training.py", line 1021, in train_function *
return step_function(self, iterator)
File "C:\Users\Dion\.conda\envs\KerASS\lib\site-packages\keras\engine\training.py", line 1010, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\Dion\.conda\envs\KerASS\lib\site-packages\keras\engine\training.py", line 1000, in run_step **
outputs = model.train_step(data)
File "C:\Users\Dion\.conda\envs\KerASS\lib\site-packages\keras\engine\training.py", line 859, in train_step
y_pred = self(x, training=True)
File "C:\Users\Dion\.conda\envs\KerASS\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Dion\.conda\envs\KerASS\lib\site-packages\keras\engine\input_spec.py", line 264, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" is '
ValueError: Input 0 of layer "sequential_1" is incompatible with the layer: expected shape=(None, 192, 192, 3), found shape=(192, 192, 3)
How to convert in python SNMPv2-SMI.enterprises.9.1.283 to dot oid?
SNMPv2-SMI.enterprises.9.1.283 -> 1.3.6.1.4.1.9.1.283
Thanks,
Alexey
You can get usufal data by:
print(o.getLabel())
print(o.getMibNode())
print(o.getMibSymbol())
print(o.getOid())
print(o.prettyPrint())
pysnmp implementation to resolve mib names to OID
from pysnmp.smi import builder, view, rfc1902, error
mibBuilder = builder.MibBuilder()
mibView = view.MibViewController(mibBuilder)
mibVar = rfc1902.ObjectIdentity('SNMPv2-SMI', 'enterprises', 9, 1, 283)
mibVar.resolveWithMib(mibView)
print(mibVar.prettyPrint()) # prints SNMPv2-SMI::enterprises.9.1.283
print(tuple(mibVar)) # prints (1, 3, 6, 1, 4, 1, 9, 1, 283)
print(str(mibVar)) # prints 1.3.6.1.4.1.9.1.283
Reference
I'd like to remove a specific edge (specific color) from a MultiGraph.
How can I do that?
Following code does not work.
#!/usr/bin/env python
import matplotlib.pyplot as plt
import networkx as nx
G = nx.MultiGraph()
# the_colored_graph.add_edge(v1, v2, "red")
G.add_edge(1, 2, color="red")
G.add_edge(2, 3, color="red")
G.add_edge(4, 2, color="green")
G.add_edge(2, 4, color="blue")
print (G.edges(data=True))
# G.remove_edge(2, 4, color="green")
#
selected_edge = [(u,v) for u,v,e in G.edges(data=True) if e['color'] == 'green']
print (selected_edge)
G.remove_edge(selected_edge[0][0], selected_edge[0][1])
print (G.edges(data=True))
nx.draw(G)
plt.show()
When constructing the multigraph, assign a "key" attribute to each edge (the key could be anything that disambiguates the parallel edges - say, the color):
G.add_edge(1, 2, color="red", key='red')
Remove an edges by specifying the end nodes and the key:
G.remove_edge(1, 2, key='red')
I am new to Keras and I am trying to make a Neuronal Network to recognize 38 cases. I created such a model, but it just does not work. There is some problem with last layer I think. I checked summary and it looks like output of last layers is 38 as it should. Can someone help me with making it work?
My code is:
model = Sequential()
model.add(Convolution2D(16, 5, 5, border_mode='valid', input_shape=(168, 192, 3)) )
model.add( Activation('relu') )
model.add( MaxPooling2D(2,2) )
model.add( Convolution2D(16, 5, 5) )
model.add( Activation('relu') )
model.add( MaxPooling2D(2,2) )
model.add( Flatten() )
model.add( Dense(512, activation='relu'))
model.add(Dense(38, activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer=adam(0.001),metrics=['accuracy'])
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'data/train',
target_size=(168, 192),
batch_size=38,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
'data/validation',
target_size=(168, 192),
batch_size=38,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=2000,
epochs=10,
validation_data=validation_generator,
validation_steps=800)
and the error looks like:
ValueError: Error when checking target: expected dense_129 to have shape (None, 38) but got array with shape (38, 1)
According to Keras documentation of from_from_directory, the specified directory ('data/train' in your case) should contain one subdirectory per class.
Since the error is saying the model is getting an array of shape (38, 1), this means you do not have 38 folders with data/train. (Note do not confuse that the first 38 dimension is the batch size, which coincidentally you have set it to same as number of classes, but does not have to be).
So you should either reaarange your subfolders into one class per subfolder, or load data manually, and flow from memory.
Dears, does anyone has an ideia why 'dropout_rate' and 'learning_rate' returne only 0 and does not search for the range I gave when I am doing a RandomizedSearchCV on the hyperparameters?
Here is my code for a ANN using keras/tensoflow:
# Create the model
def create_model(neurons = 1, init_mode = 'uniform', activation='relu', inputDim = 8792, dropout_rate=0.7, learn_rate=0.01, momentum=0, weight_constraint=0): #, learn_rate=0.01, momentum=0):
model = Sequential()
model.add(Dense(neurons, input_dim=inputDim, kernel_initializer=init_mode, activation=activation, kernel_constraint=maxnorm(weight_constraint), kernel_regularizer=regularizers.l2(0.001))) # one inner layer
#model.add(Dense(neurons, input_dim=inputDim, activation=activation)) # second inner layer
model.add(Dropout(dropout_rate))
model.add(Dense(1, activation='sigmoid'))
optimizer = RMSprop(lr=learn_rate)
# compile model
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
return model
# model
model = KerasClassifier(build_fn=create_model, verbose=0)
# Define K-fold cross validation test harness
kfold = StratifiedKFold(n_splits=3, shuffle=True, random_state=seed)
for train, test in kfold.split(X_train, Y_train):
print("TRAIN:", train, "VALIDATION:", test)
# Define Hyperparameters
# specify parameters and distributions to sample from
from scipy.stats import randint as sp_randint
param_dist = {'neurons': sp_randint(300, 360), #, 175, 180, 185, 190, 195, 200],
'learn_rate': sp_randint (0.001, 0.01),
'batch_size': sp_randint(50, 60),
'epochs': sp_randint(20, 30),
'dropout_rate': sp_randint(0.2, 0.8),
'weight_constraint': sp_randint(3, 8)
}
# run randomized search
n_iter_search = 100
print("[INFO] Starting training digits")
print("[INFO] Tuning hyper-parameters for accuracy")
grid = RandomizedSearchCV(estimator=model, param_distributions=param_dist,
n_iter=n_iter_search, n_jobs=10, cv=kfold)
start = time.time()
grid_result = grid.fit(X_train, Y_train)
print("[INFO] GridSearch took {:.2f} seconds".format(time.time() - start))
My answer:
[INFO] GridSearch took 1164.39 seconds
[INFO] GridSearch best score 1.000000 using parameters: {'batch_size': 54, 'dropout_rate': 0, 'epochs': 20, 'learn_rate': 0, 'neurons': 331, 'weight_constraint': 7}
[INFO] Grid scores on development set:
0.614679 (0.034327) with: {'batch_size': 54, 'dropout_rate': 0, 'epochs': 29, 'learn_rate': 0, 'neurons': 354, 'weight_constraint': 6}
0.883792 (0.008650) with: {'batch_size': 53, 'dropout_rate': 0, 'epochs': 27, 'learn_rate': 0, 'neurons': 339, 'weight_constraint': 7}
0.256881 (0.012974) with: {'batch_size': 59, 'dropout_rate': 0, 'epochs': 27, 'learn_rate': 0, 'neurons': 308, 'weight_constraint': 4}
...
Thanks for helping.
0.2 and 0.8 are not integers, so when you use sp_randint(0.2, 0.8), these are converted to integers so its the same as sp_randint(0, 0). You have to use an equivalent function that generates floating point numbers, not integers.
For example, you can use a uniform distribution (uniform from scipy.stats) to generate real numbers.