TypeError: __init__() got an unexpected keyword argument 'Log_dir'












1












$begingroup$


I am trying to build model to convert Sign Language to text. I am facing some problem while trying to create and using tensorboard object inorder to visually see the output and optimize my model.



from tensorflow.keras.layers import Dense, Activation, Conv2D, Flatten, MaxPooling2D,Dropout

from tensorflow.keras.models import Sequential

from tensorflow.keras.utils import normalize

from tensorflow.keras.callbacks import TensorBoard

import numpy as np

import tensorflow as tf

import pickle

import cv2

import time

# Load the dataset



X = pickle.load(open("X_ab.pickle", "rb"))

Y = pickle.load(open("Y_ab.pickle", "rb"))



modelname = "a-z{}".format(int(time.time()))

board = TensorBoard(Log_dir="logs/{}".format(modelname))



# Scaling the data. /255 since data is image data

X = normalize(X, axis=1)

print("({})".format(X.shape[0]/2400)+str(X.shape))  # (2400,50,50,1) - n,y,x,c





model = Sequential()

model.add(Conv2D(64, (3, 3), input_shape=X.shape[1:]))  # 64 3,3

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2))) #

model.add(Dropout(0.40))

model.add(Conv2D(128, (3, 3)))  # 5,5

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))



# EXTRA

model.add(Conv2D(64, (3, 3)))  # 5,5

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))







# remember to  flatten the data since the data is 2d and dense accepts 1d data

model.add(Flatten())

#model.add(Dense(128)) # 64 to 32



model.add(Dense(26))  # OG 1

model.add(Activation('sigmoid'))  # sigmoid





model.compile(loss='sparse_categorical_crossentropy',

              optimizer='adam',

              metrics=['accuracy'])



# batch size should be kept a little low(20-200) to avoid negative results



model.fit(X, Y, batch_size=30, epochs=10, validation_split=0.2,callbacks = [board])  # OG 30

model.save("modelname")





i = 6

while i <= 10:

    model.fit(X, Y, batch_size=30, epochs=i, validation_split=0.2,callbacks = [board])  # OG 30

    model.save("{} - ({}).model".format(modelname,i))

    i = i+1


The error which I am getting is as follow:



python3 model_a-z.py

Traceback (most recent call last):

  File "model_a-z.py", line 16, in <module>

    board = TensorBoard(Log_dir="logs/{}".format(modelname))

    TypeError: __init__() got an unexpected keyword argument 'Log_dir'





machine-learning deep-learning keras tensorflow machine-learning-model







share|improve this question









$endgroup$












  • $begingroup$
    I've already tried reinstallin protobuf(current version 3.6) tensorflow 1.12.0, tensorboard 12.2.2 and python 3.6
    $endgroup$
    – huzefa Ratlamwala
    yesterday








  • 1




    $begingroup$
    Keras/TensorBoard is looking for "log_dir" rather than "Log_dir", isn't it?
    $endgroup$
    – redhqs
    yesterday
















1












$begingroup$


I am trying to build model to convert Sign Language to text. I am facing some problem while trying to create and using tensorboard object inorder to visually see the output and optimize my model.



from tensorflow.keras.layers import Dense, Activation, Conv2D, Flatten, MaxPooling2D,Dropout

from tensorflow.keras.models import Sequential

from tensorflow.keras.utils import normalize

from tensorflow.keras.callbacks import TensorBoard

import numpy as np

import tensorflow as tf

import pickle

import cv2

import time

# Load the dataset



X = pickle.load(open("X_ab.pickle", "rb"))

Y = pickle.load(open("Y_ab.pickle", "rb"))



modelname = "a-z{}".format(int(time.time()))

board = TensorBoard(Log_dir="logs/{}".format(modelname))



# Scaling the data. /255 since data is image data

X = normalize(X, axis=1)

print("({})".format(X.shape[0]/2400)+str(X.shape))  # (2400,50,50,1) - n,y,x,c





model = Sequential()

model.add(Conv2D(64, (3, 3), input_shape=X.shape[1:]))  # 64 3,3

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2))) #

model.add(Dropout(0.40))

model.add(Conv2D(128, (3, 3)))  # 5,5

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))



# EXTRA

model.add(Conv2D(64, (3, 3)))  # 5,5

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))







# remember to  flatten the data since the data is 2d and dense accepts 1d data

model.add(Flatten())

#model.add(Dense(128)) # 64 to 32



model.add(Dense(26))  # OG 1

model.add(Activation('sigmoid'))  # sigmoid





model.compile(loss='sparse_categorical_crossentropy',

              optimizer='adam',

              metrics=['accuracy'])



# batch size should be kept a little low(20-200) to avoid negative results



model.fit(X, Y, batch_size=30, epochs=10, validation_split=0.2,callbacks = [board])  # OG 30

model.save("modelname")





i = 6

while i <= 10:

    model.fit(X, Y, batch_size=30, epochs=i, validation_split=0.2,callbacks = [board])  # OG 30

    model.save("{} - ({}).model".format(modelname,i))

    i = i+1


The error which I am getting is as follow:



python3 model_a-z.py

Traceback (most recent call last):

  File "model_a-z.py", line 16, in <module>

    board = TensorBoard(Log_dir="logs/{}".format(modelname))

    TypeError: __init__() got an unexpected keyword argument 'Log_dir'





machine-learning deep-learning keras tensorflow machine-learning-model







share|improve this question









$endgroup$












  • $begingroup$
    I've already tried reinstallin protobuf(current version 3.6) tensorflow 1.12.0, tensorboard 12.2.2 and python 3.6
    $endgroup$
    – huzefa Ratlamwala
    yesterday








  • 1




    $begingroup$
    Keras/TensorBoard is looking for "log_dir" rather than "Log_dir", isn't it?
    $endgroup$
    – redhqs
    yesterday














1












1








1





$begingroup$


I am trying to build model to convert Sign Language to text. I am facing some problem while trying to create and using tensorboard object inorder to visually see the output and optimize my model.



from tensorflow.keras.layers import Dense, Activation, Conv2D, Flatten, MaxPooling2D,Dropout

from tensorflow.keras.models import Sequential

from tensorflow.keras.utils import normalize

from tensorflow.keras.callbacks import TensorBoard

import numpy as np

import tensorflow as tf

import pickle

import cv2

import time

# Load the dataset



X = pickle.load(open("X_ab.pickle", "rb"))

Y = pickle.load(open("Y_ab.pickle", "rb"))



modelname = "a-z{}".format(int(time.time()))

board = TensorBoard(Log_dir="logs/{}".format(modelname))



# Scaling the data. /255 since data is image data

X = normalize(X, axis=1)

print("({})".format(X.shape[0]/2400)+str(X.shape))  # (2400,50,50,1) - n,y,x,c





model = Sequential()

model.add(Conv2D(64, (3, 3), input_shape=X.shape[1:]))  # 64 3,3

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2))) #

model.add(Dropout(0.40))

model.add(Conv2D(128, (3, 3)))  # 5,5

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))



# EXTRA

model.add(Conv2D(64, (3, 3)))  # 5,5

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))







# remember to  flatten the data since the data is 2d and dense accepts 1d data

model.add(Flatten())

#model.add(Dense(128)) # 64 to 32



model.add(Dense(26))  # OG 1

model.add(Activation('sigmoid'))  # sigmoid





model.compile(loss='sparse_categorical_crossentropy',

              optimizer='adam',

              metrics=['accuracy'])



# batch size should be kept a little low(20-200) to avoid negative results



model.fit(X, Y, batch_size=30, epochs=10, validation_split=0.2,callbacks = [board])  # OG 30

model.save("modelname")





i = 6

while i <= 10:

    model.fit(X, Y, batch_size=30, epochs=i, validation_split=0.2,callbacks = [board])  # OG 30

    model.save("{} - ({}).model".format(modelname,i))

    i = i+1


The error which I am getting is as follow:



python3 model_a-z.py

Traceback (most recent call last):

  File "model_a-z.py", line 16, in <module>

    board = TensorBoard(Log_dir="logs/{}".format(modelname))

    TypeError: __init__() got an unexpected keyword argument 'Log_dir'





machine-learning deep-learning keras tensorflow machine-learning-model







share|improve this question









$endgroup$




I am trying to build model to convert Sign Language to text. I am facing some problem while trying to create and using tensorboard object inorder to visually see the output and optimize my model.



from tensorflow.keras.layers import Dense, Activation, Conv2D, Flatten, MaxPooling2D,Dropout

from tensorflow.keras.models import Sequential

from tensorflow.keras.utils import normalize

from tensorflow.keras.callbacks import TensorBoard

import numpy as np

import tensorflow as tf

import pickle

import cv2

import time

# Load the dataset



X = pickle.load(open("X_ab.pickle", "rb"))

Y = pickle.load(open("Y_ab.pickle", "rb"))



modelname = "a-z{}".format(int(time.time()))

board = TensorBoard(Log_dir="logs/{}".format(modelname))



# Scaling the data. /255 since data is image data

X = normalize(X, axis=1)

print("({})".format(X.shape[0]/2400)+str(X.shape))  # (2400,50,50,1) - n,y,x,c





model = Sequential()

model.add(Conv2D(64, (3, 3), input_shape=X.shape[1:]))  # 64 3,3

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2))) #

model.add(Dropout(0.40))

model.add(Conv2D(128, (3, 3)))  # 5,5

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))



# EXTRA

model.add(Conv2D(64, (3, 3)))  # 5,5

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))







# remember to  flatten the data since the data is 2d and dense accepts 1d data

model.add(Flatten())

#model.add(Dense(128)) # 64 to 32



model.add(Dense(26))  # OG 1

model.add(Activation('sigmoid'))  # sigmoid





model.compile(loss='sparse_categorical_crossentropy',

              optimizer='adam',

              metrics=['accuracy'])



# batch size should be kept a little low(20-200) to avoid negative results



model.fit(X, Y, batch_size=30, epochs=10, validation_split=0.2,callbacks = [board])  # OG 30

model.save("modelname")





i = 6

while i <= 10:

    model.fit(X, Y, batch_size=30, epochs=i, validation_split=0.2,callbacks = [board])  # OG 30

    model.save("{} - ({}).model".format(modelname,i))

    i = i+1


The error which I am getting is as follow:



python3 model_a-z.py

Traceback (most recent call last):

  File "model_a-z.py", line 16, in <module>

    board = TensorBoard(Log_dir="logs/{}".format(modelname))

    TypeError: __init__() got an unexpected keyword argument 'Log_dir'





machine-learning deep-learning keras tensorflow machine-learning-model




machine-learning deep-learning keras tensorflow machine-learning-model






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked yesterday









huzefa Ratlamwalahuzefa Ratlamwala

182




182












  • $begingroup$
    I've already tried reinstallin protobuf(current version 3.6) tensorflow 1.12.0, tensorboard 12.2.2 and python 3.6
    $endgroup$
    – huzefa Ratlamwala
    yesterday








  • 1




    $begingroup$
    Keras/TensorBoard is looking for "log_dir" rather than "Log_dir", isn't it?
    $endgroup$
    – redhqs
    yesterday


















  • $begingroup$
    I've already tried reinstallin protobuf(current version 3.6) tensorflow 1.12.0, tensorboard 12.2.2 and python 3.6
    $endgroup$
    – huzefa Ratlamwala
    yesterday








  • 1




    $begingroup$
    Keras/TensorBoard is looking for "log_dir" rather than "Log_dir", isn't it?
    $endgroup$
    – redhqs
    yesterday
















$begingroup$
I've already tried reinstallin protobuf(current version 3.6) tensorflow 1.12.0, tensorboard 12.2.2 and python 3.6
$endgroup$
– huzefa Ratlamwala
yesterday






$begingroup$
I've already tried reinstallin protobuf(current version 3.6) tensorflow 1.12.0, tensorboard 12.2.2 and python 3.6
$endgroup$
– huzefa Ratlamwala
yesterday






1




1




$begingroup$
Keras/TensorBoard is looking for "log_dir" rather than "Log_dir", isn't it?
$endgroup$
– redhqs
yesterday




$begingroup$
Keras/TensorBoard is looking for "log_dir" rather than "Log_dir", isn't it?
$endgroup$
– redhqs
yesterday










0






active

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