I am trying to merge in tf.keras . What is the equivalent.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Concatenate()([sentrnn, qrnn]))
getting following exception
A Concatenate layer should be called on a list of at least 2 inputs
trying to convert this code I found on one of the sites
model.add(Merge([sentrnn, qrnn], mode='concat'))
model.add(Merge([sentrnn, qrnn], mode='sum'))
sentrnn = Sequential()
sentrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE, mask_zero=True))
sentrnn.add(RNN(SENT_HIDDEN_SIZE, return_sequences=False))
sentrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE, input_length=story_maxlen, mask_zero=True))
sentrnn.add(Dropout(0.3))
qrnn = Sequential()
qrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE))
qrnn.add(RNN(QUERY_HIDDEN_SIZE, return_sequences=False))
qrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE, input_length=query_maxlen))
qrnn.add(Dropout(0.3))
qrnn.add(RNN(EMBED_HIDDEN_SIZE, return_sequences=False))
qrnn.add(RepeatVector(story_maxlen))
model = Sequential()
model.add(Merge([sentrnn, qrnn], mode='concat'))
model.add(Merge([sentrnn, qrnn], mode='sum'))
model.add(RNN(EMBED_HIDDEN_SIZE, return_sequences=False))
model.add(Dropout(0.3))
model.add(Dense(vocab_size, activation='softmax'))
Related
I am an absolute beginner in neural networks and I have problems passing data into the network.
This is the model:
model = keras.Sequential([keras.layers.Flatten(input_shape=(300,150,3)),keras.layers.Dense(128,activation='relu'),keras.layers.Dense(10,activation='softmax')])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
The data consists of images with labels as tuples. Its a mutli-label dataset, where each label, e.g. varroa_output, contains 1 if the characterisitic was present in the image and a 0 if it wasn't. This is the train_batch I tried to feed into the neural network:
<BatchDataset element_spec=(TensorSpec(shape=(None, 300, 150, 3), dtype=tf.uint8, name=None), {'cooling_output': TensorSpec(shape=(None,), dtype=tf.float64, name=None), 'pollen_output': TensorSpec(shape=(None,), dtype=tf.float64, name=None), 'varroa_output': TensorSpec(shape=(None,), dtype=tf.float64, name=None), 'wasps_output': TensorSpec(shape=(None,), dtype=tf.float64, name=None)})>
This is how I tried to train the model:
history = model.fit(train_batches,epochs=5,validation_data=validation_batches)
acc = history.history['accuracy']
print(acc)
And the following error occurs:
ValueError: Found unexpected losses or metrics that do not correspond to any Model output: dict_keys(['cooling_output', 'pollen_output', 'varroa_output', 'wasps_output']). Valid mode output names: ['dense_11']. Received struct is: {'cooling_output': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=float64>, 'pollen_output': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=float64>, 'varroa_output': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=float64>, 'wasps_output': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=float64>}.
I am sure there is an obvious explanation but I am an absolute beginner and would appreciate any help.
I am trying to run a hyperparameters optimization with Talos. As I have a lot of parameters to test, I want to use a 'grid_downsample' argument that will select 30% of all possible hyperparameters combinations. However when I run my code I get: TypeError: __init__() got an unexpected keyword argument 'grid_downsample'
I tested the code below without the 'grid_downsample' option and with less hyperparameters.
#load data
data = pd.read_csv('data.txt', sep="\t", encoding = "latin1")
# split into input (X) and output (y) variables
Y = np.array(data['Y'])
data_bis = data.drop(['Y'], axis = 1)
X = np.array(data_bis)
p = {'activation':['relu'],
'optimizer': ['Nadam'],
'first_hidden_layer': [12],
'second_hidden_layer': [12],
'batch_size': [20],
'epochs': [10,20],
'dropout_rate':[0.0, 0.2]}
def dnn_model(x_train, y_train, x_val, y_val, params):
model = Sequential()
#input layer
model.add(Dense(params['first_hidden_layer'], input_shape=(1024,)))
model.add(Dropout(params['dropout_rate']))
model.add(Activation(params['activation']))
#hidden layer 2
model.add(Dense(params['second_hidden_layer']))
model.add(Dropout(params['dropout_rate']))
model.add(Activation(params['activation']))
# output layer with one node
model.add(Dense(1))
model.add(Activation(params['activation']))
# Compile model
model.compile(loss='binary_crossentropy', optimizer=params['optimizer'], metrics=['accuracy'])
out = model.fit(x_train, y_train,
batch_size=params['batch_size'],
epochs=params['epochs'],
validation_data=[x_val, y_val],
verbose=0)
return out, model
scan_object = ta.Scan(X, Y, model=dnn_model, params=p, experiment_name="test")
reporting = ta.Reporting(scan_object)
report = reporting.data
report.to_csv('./Random_search/dnn/report_talos.txt', sep = '\t')
This code works well. If I change the scan_object as the end to: scan_object = ta.Scan(X, Y, model=dnn_model, grid_downsample=0.3, params=p, experiment_name="test"), it gives me the error: TypeError: __init__() got an unexpected keyword argument 'grid_downsample' while I was expecting to have the same results format as a normal grid search but with less combinations. What am I missing? Did the name of the argument change? I'm using Talos 0.6.3 in a conda environment.
Thank you!
might be too late for you now but they've switched it to fraction_limit. It would give this for you
scan_object = ta.Scan(X, Y, model=dnn_model, params=p, experiment_name="test", fraction_limit = 0.1)
Sadly, the doc isn't well updated
Check out their examples on GitHub:
https://github.com/autonomio/talos/blob/master/examples/Hyperparameter%20Optimization%20with%20Keras%20for%20the%20Iris%20Prediction.ipynb
Do you guys know where or how to obtain the 0.47MB version of SqueezeNet ?
In other words, how to make the weights bitwidth to be 6 instead of 8 ?
I cannot find the modification spot in this SqueezeNet generation code.
In this following method, I got 0.77 MB Model! Lets assume we have a SqueezeNet_model. We can convert SqueezeNet to Tensorflow Lite Model.
converter = tf.lite.TFLiteConverter.from_keras_model(SqueezeNet_model)
open("SqueezeNet_model.tflite", "wb").write(tflite_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_quant_model = converter.convert()
Then, we can use POST quantization to decrease the size of model!
open("SqueezeNet_Quant_model.tflite", "wb").write(tflite_quant_model)
print("Quantized model in Mb:", os.path.getsize('SqueezeNet_Quant_model.tflite') / float(2**20)) // I got 0.77 MB model
Finally, we can test our model with:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="SqueezeNet_Quant_model.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test model on some input data.
input_shape = input_details[0]['shape']
acc=0
for i in range(len(x_test)):
input_data = np.array(x_test[i].reshape(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
if(np.argmax(output_data) == np.argmax(y_test[i])):
acc+=1
acc = acc/len(x_test)
print(acc*100)
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.
I am trying to build a recurrent network which classifies sequences (multidimensional data streams). I must be missing something, since while running my code:
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Activation
import numpy as np
ils = 10 # input layer size
ilt = 11 # input layer time steps
hls = 12 # hidden layer size
nhl = 2 # number of hidden layers
ols = 1 # output layer size
p = 0.2 # dropout probability
f_a = 'relu' # activation function
opt = 'rmsprop' # optimizing function
#
# Building the model
#
model = Sequential()
# The input layer
model.add(LSTM(hls, input_shape=(ilt, ils), return_sequences=True))
model.add(Activation(f_a))
model.add(Dropout(p))
# Hidden layers
for i in range(nhl - 1):
model.add(LSTM(hls, return_sequences=True))
model.add(Activation(f_a))
model.add(Dropout(p))
# Output layer
model.add(LSTM(ols, return_sequences=False))
model.add(Activation('softmax'))
model.compile(optimizer=opt, loss='binary_crossentropy')
#
# Making test data and fitting the model
#
m_train, n_class = 1000, 2
data = np.array(np.random.random((m_train, ilt, ils)))
labels = np.random.randint(n_class, size=(m_train, 1))
model.fit(data, labels, nb_epoch=10, batch_size=32)
I get output (truncated):
Using Theano backend.
line 611, in __call__
node = self.make_node(*inputs, **kwargs)
File "/home/koala/.local/lib/python2.7/site-packages/theano/scan_module/scan_op.py", line 430, in make_node
new_inputs.append(format(outer_seq, as_var=inner_seq))
File "/home/koala/.local/lib/python2.7/site-packages/theano/scan_module/scan_op.py", line 422, in format
rval = tmp.filter_variable(rval)
File "/home/koala/.local/lib/python2.7/site-packages/theano/tensor/type.py", line 233, in filter_variable
self=self))
TypeError: Cannot convert Type TensorType(float32, 3D) (of Variable Subtensor{:int64:}.0) into Type TensorType(float32, (False, False, True)). You can try to manually convert Subtensor{:int64:}.0 into a TensorType(float32, (False, False, True)).
Is this a problem with the data format at all.
For me the problem was fixed when I went and tried it on my real dataset. The difference being that in the real dataset I have more than 1 label. So an example of dataset on which this code works is:
(...)
ols = 2 # Output layer size
(...)
m_train, n_class = 1000, ols
data = np.array(np.random.random((m_train, ilt, ils)))
labels = np.random.randint(n_class, size=(m_train, 1))
# Make labels onehot
onehot_labels = np.zeros(shape=(labels.shape[0], ols))
onehot_labels[np.arange(labels.shape[0]), labels.astype(np.int)] = 1