tensorflow estimator accuracy and loss is zero











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0
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My model's accuracy and loss are evaluating to 0.

The global steps should be 1625 but it's 1.

The acc and loss shouldn't be equal to 0 as both of them are contradicting each other.



My input function,keras estimator,train_and_evaluate are as follows:



def make_input_fn(addrs,labels,batch_size,mode):



 filename_dataset = tf.data.Dataset.from_tensor_slices((addrs,labels))     



 dataset = filename_dataset.apply(tf.contrib.data.map_and_batch(lambda 

 addrs, labels: tuple(tf.py_func(

    process, [addrs, labels], [tf.uint8, labels.dtype])),batch_size,



 num_parallel_batches=2,



 drop_remainder=False))

 if mode == tf.estimator.ModeKeys.TRAIN:

  num_epochs = None # indefinitely

  dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(buffer_size = 10000))

 else:

  num_epochs = 1

  dataset = dataset.repeat(num_epochs)



 dataset = dataset.prefetch(buffer_size=batch_size)

 images,labels = dataset.make_one_shot_iterator().get_next()

 images.set_shape([None,512,512,3])

 labels.set_shape([None,1])

 return images,labels



def keras_estimator(model_dir,config):

 base_model = Xception(weights='imagenet', include_top=False,input_shape = 

  (512,512,3),classes = 5)

 x = base_model.output

 x = GlobalAveragePooling2D()(x)



 x = Dense(1024, activation='relu')(x)

 x = Dropout(0.2)(x)

 x = Dense(256, activation='relu')(x)

 x = Dropout(0.2)(x)



 predictions = Dense(5, activation='softmax')(x)





 model = Model(inputs=base_model.input, outputs=predictions)





 for layer in base_model.layers:

   layer.trainable = False

 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', 

       metrics=['acc'])





 estimator=tf.keras.estimator.model_to_estimator(keras_model=model,

      model_dir=model_dir,

      config=config)

 return estimator



def train_and_evaluate(model_dir):

 t_batch_size = 512

 e_batch_size = 64

 num_epochs = 25

 import pandas as pd

 df = pd.read_csv('/content/trainLabels.csv')

 from random import shuffle

 addrs = ['/content/train/train/' + str(df.iloc[i]['image']) + '.jpeg' for i 

 in range(len(df))]

 labels = df['level'].values.tolist()

 c = list(zip(addrs, labels))

 shuffle(c)

 addrs1, labels1 = zip(*c)

 train_addrs = addrs1[0 : int(0.9 * len(addrs))]

 train_labels = labels1[0 : int(0.9 * len(labels))]

 val_addrs = addrs1[ int(0.9 * len(addrs)) : ]

 val_labels = labels1[ int(0.9 * len(addrs)) : ]

 train_addrs = list(train_addrs)

 train_labels = list(train_labels)

 val_addrs = list(val_addrs)

 val_labels = list(val_labels)



 run_config = tf.estimator.RunConfig(save_checkpoints_secs=300)



 estimator = keras_estimator(model_dir,run_config)



 t_max_steps = (len(train_addrs) // t_batch_size) * num_epochs



 train_spec = tf.estimator.TrainSpec(input_fn = lambda : 

 make_input_fn(train_addrs,train_labels,

 t_batch_size,mode=tf.estimator.ModeKeys.TRAIN),max_steps = t_max_steps)



 eval_spec = tf.estimator.EvalSpec(input_fn = lambda : 

 make_input_fn(val_addrs,val_labels,

 e_batch_size,mode=tf.estimator.ModeKeys.EVAL),steps = 

 None,start_delay_secs=10,

    throttle_secs=300)





 tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)


Here are the log files:




INFO:tensorflow:Running training and evaluation locally
(non-distributed). INFO:tensorflow:Start train and evaluate loop. The
evaluate will happen after every checkpoint. Checkpoint frequency is
determined based on RunConfig arguments: save_checkpoints_steps None
or save_checkpoints_secs 300. WARNING:tensorflow:From
:9: map_and_batch (from
tensorflow.contrib.data.python.ops.batching) is deprecated and will be
removed in a future version. Instructions for updating: Use
tf.data.experimental.map_and_batch(...). WARNING:tensorflow:From
:12: shuffle_and_repeat (from
tensorflow.contrib.data.python.ops.shuffle_ops) is deprecated and will
be removed in a future version. Instructions for updating: Use
tf.data.experimental.shuffle_and_repeat(...). INFO:tensorflow:Calling
model_fn. INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings:
WarmStartSettings(ckpt_to_initialize_from='/content/training/keras/keras_model.ckpt',
vars_to_warm_start='.*', var_name_to_vocab_info={},
var_name_to_prev_var_name={}) INFO:tensorflow:Warm-starting from:
('/content/training/keras/keras_model.ckpt',)
INFO:tensorflow:Warm-starting variable: dense/kernel; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: dense/bias;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
dense_1/kernel; prev_var_name: Unchanged INFO:tensorflow:Warm-starting
variable: dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: dense_2/kernel; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: dense_2/bias;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
Adam/iterations; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: Adam/lr; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: Adam/beta_1;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
Adam/beta_2; prev_var_name: Unchanged INFO:tensorflow:Warm-starting
variable: Adam/decay; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_1; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_2;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_3; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_4;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_5; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_6;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_7; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_8;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_9; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_10;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_11; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_12;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_13; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_14;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_15; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_16;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_17; prev_var_name: Unchanged
INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was
finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done
running local_init_op. INFO:tensorflow:Saving checkpoints for 0 into
/content/training/model.ckpt. INFO:tensorflow:Saving checkpoints for 1
into /content/training/model.ckpt. INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting
evaluation at 2018-11-05-13:21:17 INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from
/content/training/model.ckpt-1 INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished
evaluation at 2018-11-05-13:22:08 INFO:tensorflow:Saving dict for
global step 1: acc = 0.0, global_step = 1, loss = 0.0
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1:
/content/training/model.ckpt-1 INFO:tensorflow:Loss for final step:
None.







python tensorflow deep-learning tensorflow-datasets tensorflow-estimator







share|improve this question









New contributor




Aarya Patel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.




















  • Welcome to SO, have you tried debugging and narrowing down the possible issues? At the moment it's close to "my code is broken, please fix", which is unlikely to attract answers. Try to figure out what you think is causing the accuracy and loss issues, and then edit this to include a specific question about a section of your code.
    – Shayn
    yesterday










  • I'm not able to figure out how I debug my code. Everything seems to be alright.
    – Aarya Patel
    2 hours ago















up vote
0
down vote

favorite












My model's accuracy and loss are evaluating to 0.

The global steps should be 1625 but it's 1.

The acc and loss shouldn't be equal to 0 as both of them are contradicting each other.



My input function,keras estimator,train_and_evaluate are as follows:



def make_input_fn(addrs,labels,batch_size,mode):



 filename_dataset = tf.data.Dataset.from_tensor_slices((addrs,labels))     



 dataset = filename_dataset.apply(tf.contrib.data.map_and_batch(lambda 

 addrs, labels: tuple(tf.py_func(

    process, [addrs, labels], [tf.uint8, labels.dtype])),batch_size,



 num_parallel_batches=2,



 drop_remainder=False))

 if mode == tf.estimator.ModeKeys.TRAIN:

  num_epochs = None # indefinitely

  dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(buffer_size = 10000))

 else:

  num_epochs = 1

  dataset = dataset.repeat(num_epochs)



 dataset = dataset.prefetch(buffer_size=batch_size)

 images,labels = dataset.make_one_shot_iterator().get_next()

 images.set_shape([None,512,512,3])

 labels.set_shape([None,1])

 return images,labels



def keras_estimator(model_dir,config):

 base_model = Xception(weights='imagenet', include_top=False,input_shape = 

  (512,512,3),classes = 5)

 x = base_model.output

 x = GlobalAveragePooling2D()(x)



 x = Dense(1024, activation='relu')(x)

 x = Dropout(0.2)(x)

 x = Dense(256, activation='relu')(x)

 x = Dropout(0.2)(x)



 predictions = Dense(5, activation='softmax')(x)





 model = Model(inputs=base_model.input, outputs=predictions)





 for layer in base_model.layers:

   layer.trainable = False

 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', 

       metrics=['acc'])





 estimator=tf.keras.estimator.model_to_estimator(keras_model=model,

      model_dir=model_dir,

      config=config)

 return estimator



def train_and_evaluate(model_dir):

 t_batch_size = 512

 e_batch_size = 64

 num_epochs = 25

 import pandas as pd

 df = pd.read_csv('/content/trainLabels.csv')

 from random import shuffle

 addrs = ['/content/train/train/' + str(df.iloc[i]['image']) + '.jpeg' for i 

 in range(len(df))]

 labels = df['level'].values.tolist()

 c = list(zip(addrs, labels))

 shuffle(c)

 addrs1, labels1 = zip(*c)

 train_addrs = addrs1[0 : int(0.9 * len(addrs))]

 train_labels = labels1[0 : int(0.9 * len(labels))]

 val_addrs = addrs1[ int(0.9 * len(addrs)) : ]

 val_labels = labels1[ int(0.9 * len(addrs)) : ]

 train_addrs = list(train_addrs)

 train_labels = list(train_labels)

 val_addrs = list(val_addrs)

 val_labels = list(val_labels)



 run_config = tf.estimator.RunConfig(save_checkpoints_secs=300)



 estimator = keras_estimator(model_dir,run_config)



 t_max_steps = (len(train_addrs) // t_batch_size) * num_epochs



 train_spec = tf.estimator.TrainSpec(input_fn = lambda : 

 make_input_fn(train_addrs,train_labels,

 t_batch_size,mode=tf.estimator.ModeKeys.TRAIN),max_steps = t_max_steps)



 eval_spec = tf.estimator.EvalSpec(input_fn = lambda : 

 make_input_fn(val_addrs,val_labels,

 e_batch_size,mode=tf.estimator.ModeKeys.EVAL),steps = 

 None,start_delay_secs=10,

    throttle_secs=300)





 tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)


Here are the log files:




INFO:tensorflow:Running training and evaluation locally
(non-distributed). INFO:tensorflow:Start train and evaluate loop. The
evaluate will happen after every checkpoint. Checkpoint frequency is
determined based on RunConfig arguments: save_checkpoints_steps None
or save_checkpoints_secs 300. WARNING:tensorflow:From
:9: map_and_batch (from
tensorflow.contrib.data.python.ops.batching) is deprecated and will be
removed in a future version. Instructions for updating: Use
tf.data.experimental.map_and_batch(...). WARNING:tensorflow:From
:12: shuffle_and_repeat (from
tensorflow.contrib.data.python.ops.shuffle_ops) is deprecated and will
be removed in a future version. Instructions for updating: Use
tf.data.experimental.shuffle_and_repeat(...). INFO:tensorflow:Calling
model_fn. INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings:
WarmStartSettings(ckpt_to_initialize_from='/content/training/keras/keras_model.ckpt',
vars_to_warm_start='.*', var_name_to_vocab_info={},
var_name_to_prev_var_name={}) INFO:tensorflow:Warm-starting from:
('/content/training/keras/keras_model.ckpt',)
INFO:tensorflow:Warm-starting variable: dense/kernel; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: dense/bias;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
dense_1/kernel; prev_var_name: Unchanged INFO:tensorflow:Warm-starting
variable: dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: dense_2/kernel; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: dense_2/bias;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
Adam/iterations; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: Adam/lr; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: Adam/beta_1;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
Adam/beta_2; prev_var_name: Unchanged INFO:tensorflow:Warm-starting
variable: Adam/decay; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_1; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_2;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_3; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_4;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_5; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_6;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_7; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_8;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_9; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_10;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_11; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_12;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_13; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_14;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_15; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_16;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_17; prev_var_name: Unchanged
INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was
finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done
running local_init_op. INFO:tensorflow:Saving checkpoints for 0 into
/content/training/model.ckpt. INFO:tensorflow:Saving checkpoints for 1
into /content/training/model.ckpt. INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting
evaluation at 2018-11-05-13:21:17 INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from
/content/training/model.ckpt-1 INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished
evaluation at 2018-11-05-13:22:08 INFO:tensorflow:Saving dict for
global step 1: acc = 0.0, global_step = 1, loss = 0.0
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1:
/content/training/model.ckpt-1 INFO:tensorflow:Loss for final step:
None.







python tensorflow deep-learning tensorflow-datasets tensorflow-estimator







share|improve this question









New contributor




Aarya Patel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.




















  • Welcome to SO, have you tried debugging and narrowing down the possible issues? At the moment it's close to "my code is broken, please fix", which is unlikely to attract answers. Try to figure out what you think is causing the accuracy and loss issues, and then edit this to include a specific question about a section of your code.
    – Shayn
    yesterday










  • I'm not able to figure out how I debug my code. Everything seems to be alright.
    – Aarya Patel
    2 hours ago













up vote
0
down vote

favorite









up vote
0
down vote

favorite











My model's accuracy and loss are evaluating to 0.

The global steps should be 1625 but it's 1.

The acc and loss shouldn't be equal to 0 as both of them are contradicting each other.



My input function,keras estimator,train_and_evaluate are as follows:



def make_input_fn(addrs,labels,batch_size,mode):



 filename_dataset = tf.data.Dataset.from_tensor_slices((addrs,labels))     



 dataset = filename_dataset.apply(tf.contrib.data.map_and_batch(lambda 

 addrs, labels: tuple(tf.py_func(

    process, [addrs, labels], [tf.uint8, labels.dtype])),batch_size,



 num_parallel_batches=2,



 drop_remainder=False))

 if mode == tf.estimator.ModeKeys.TRAIN:

  num_epochs = None # indefinitely

  dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(buffer_size = 10000))

 else:

  num_epochs = 1

  dataset = dataset.repeat(num_epochs)



 dataset = dataset.prefetch(buffer_size=batch_size)

 images,labels = dataset.make_one_shot_iterator().get_next()

 images.set_shape([None,512,512,3])

 labels.set_shape([None,1])

 return images,labels



def keras_estimator(model_dir,config):

 base_model = Xception(weights='imagenet', include_top=False,input_shape = 

  (512,512,3),classes = 5)

 x = base_model.output

 x = GlobalAveragePooling2D()(x)



 x = Dense(1024, activation='relu')(x)

 x = Dropout(0.2)(x)

 x = Dense(256, activation='relu')(x)

 x = Dropout(0.2)(x)



 predictions = Dense(5, activation='softmax')(x)





 model = Model(inputs=base_model.input, outputs=predictions)





 for layer in base_model.layers:

   layer.trainable = False

 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', 

       metrics=['acc'])





 estimator=tf.keras.estimator.model_to_estimator(keras_model=model,

      model_dir=model_dir,

      config=config)

 return estimator



def train_and_evaluate(model_dir):

 t_batch_size = 512

 e_batch_size = 64

 num_epochs = 25

 import pandas as pd

 df = pd.read_csv('/content/trainLabels.csv')

 from random import shuffle

 addrs = ['/content/train/train/' + str(df.iloc[i]['image']) + '.jpeg' for i 

 in range(len(df))]

 labels = df['level'].values.tolist()

 c = list(zip(addrs, labels))

 shuffle(c)

 addrs1, labels1 = zip(*c)

 train_addrs = addrs1[0 : int(0.9 * len(addrs))]

 train_labels = labels1[0 : int(0.9 * len(labels))]

 val_addrs = addrs1[ int(0.9 * len(addrs)) : ]

 val_labels = labels1[ int(0.9 * len(addrs)) : ]

 train_addrs = list(train_addrs)

 train_labels = list(train_labels)

 val_addrs = list(val_addrs)

 val_labels = list(val_labels)



 run_config = tf.estimator.RunConfig(save_checkpoints_secs=300)



 estimator = keras_estimator(model_dir,run_config)



 t_max_steps = (len(train_addrs) // t_batch_size) * num_epochs



 train_spec = tf.estimator.TrainSpec(input_fn = lambda : 

 make_input_fn(train_addrs,train_labels,

 t_batch_size,mode=tf.estimator.ModeKeys.TRAIN),max_steps = t_max_steps)



 eval_spec = tf.estimator.EvalSpec(input_fn = lambda : 

 make_input_fn(val_addrs,val_labels,

 e_batch_size,mode=tf.estimator.ModeKeys.EVAL),steps = 

 None,start_delay_secs=10,

    throttle_secs=300)





 tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)


Here are the log files:




INFO:tensorflow:Running training and evaluation locally
(non-distributed). INFO:tensorflow:Start train and evaluate loop. The
evaluate will happen after every checkpoint. Checkpoint frequency is
determined based on RunConfig arguments: save_checkpoints_steps None
or save_checkpoints_secs 300. WARNING:tensorflow:From
:9: map_and_batch (from
tensorflow.contrib.data.python.ops.batching) is deprecated and will be
removed in a future version. Instructions for updating: Use
tf.data.experimental.map_and_batch(...). WARNING:tensorflow:From
:12: shuffle_and_repeat (from
tensorflow.contrib.data.python.ops.shuffle_ops) is deprecated and will
be removed in a future version. Instructions for updating: Use
tf.data.experimental.shuffle_and_repeat(...). INFO:tensorflow:Calling
model_fn. INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings:
WarmStartSettings(ckpt_to_initialize_from='/content/training/keras/keras_model.ckpt',
vars_to_warm_start='.*', var_name_to_vocab_info={},
var_name_to_prev_var_name={}) INFO:tensorflow:Warm-starting from:
('/content/training/keras/keras_model.ckpt',)
INFO:tensorflow:Warm-starting variable: dense/kernel; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: dense/bias;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
dense_1/kernel; prev_var_name: Unchanged INFO:tensorflow:Warm-starting
variable: dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: dense_2/kernel; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: dense_2/bias;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
Adam/iterations; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: Adam/lr; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: Adam/beta_1;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
Adam/beta_2; prev_var_name: Unchanged INFO:tensorflow:Warm-starting
variable: Adam/decay; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_1; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_2;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_3; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_4;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_5; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_6;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_7; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_8;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_9; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_10;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_11; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_12;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_13; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_14;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_15; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_16;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_17; prev_var_name: Unchanged
INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was
finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done
running local_init_op. INFO:tensorflow:Saving checkpoints for 0 into
/content/training/model.ckpt. INFO:tensorflow:Saving checkpoints for 1
into /content/training/model.ckpt. INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting
evaluation at 2018-11-05-13:21:17 INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from
/content/training/model.ckpt-1 INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished
evaluation at 2018-11-05-13:22:08 INFO:tensorflow:Saving dict for
global step 1: acc = 0.0, global_step = 1, loss = 0.0
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1:
/content/training/model.ckpt-1 INFO:tensorflow:Loss for final step:
None.







python tensorflow deep-learning tensorflow-datasets tensorflow-estimator







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My model's accuracy and loss are evaluating to 0.

The global steps should be 1625 but it's 1.

The acc and loss shouldn't be equal to 0 as both of them are contradicting each other.



My input function,keras estimator,train_and_evaluate are as follows:



def make_input_fn(addrs,labels,batch_size,mode):



 filename_dataset = tf.data.Dataset.from_tensor_slices((addrs,labels))     



 dataset = filename_dataset.apply(tf.contrib.data.map_and_batch(lambda 

 addrs, labels: tuple(tf.py_func(

    process, [addrs, labels], [tf.uint8, labels.dtype])),batch_size,



 num_parallel_batches=2,



 drop_remainder=False))

 if mode == tf.estimator.ModeKeys.TRAIN:

  num_epochs = None # indefinitely

  dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(buffer_size = 10000))

 else:

  num_epochs = 1

  dataset = dataset.repeat(num_epochs)



 dataset = dataset.prefetch(buffer_size=batch_size)

 images,labels = dataset.make_one_shot_iterator().get_next()

 images.set_shape([None,512,512,3])

 labels.set_shape([None,1])

 return images,labels



def keras_estimator(model_dir,config):

 base_model = Xception(weights='imagenet', include_top=False,input_shape = 

  (512,512,3),classes = 5)

 x = base_model.output

 x = GlobalAveragePooling2D()(x)



 x = Dense(1024, activation='relu')(x)

 x = Dropout(0.2)(x)

 x = Dense(256, activation='relu')(x)

 x = Dropout(0.2)(x)



 predictions = Dense(5, activation='softmax')(x)





 model = Model(inputs=base_model.input, outputs=predictions)





 for layer in base_model.layers:

   layer.trainable = False

 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', 

       metrics=['acc'])





 estimator=tf.keras.estimator.model_to_estimator(keras_model=model,

      model_dir=model_dir,

      config=config)

 return estimator



def train_and_evaluate(model_dir):

 t_batch_size = 512

 e_batch_size = 64

 num_epochs = 25

 import pandas as pd

 df = pd.read_csv('/content/trainLabels.csv')

 from random import shuffle

 addrs = ['/content/train/train/' + str(df.iloc[i]['image']) + '.jpeg' for i 

 in range(len(df))]

 labels = df['level'].values.tolist()

 c = list(zip(addrs, labels))

 shuffle(c)

 addrs1, labels1 = zip(*c)

 train_addrs = addrs1[0 : int(0.9 * len(addrs))]

 train_labels = labels1[0 : int(0.9 * len(labels))]

 val_addrs = addrs1[ int(0.9 * len(addrs)) : ]

 val_labels = labels1[ int(0.9 * len(addrs)) : ]

 train_addrs = list(train_addrs)

 train_labels = list(train_labels)

 val_addrs = list(val_addrs)

 val_labels = list(val_labels)



 run_config = tf.estimator.RunConfig(save_checkpoints_secs=300)



 estimator = keras_estimator(model_dir,run_config)



 t_max_steps = (len(train_addrs) // t_batch_size) * num_epochs



 train_spec = tf.estimator.TrainSpec(input_fn = lambda : 

 make_input_fn(train_addrs,train_labels,

 t_batch_size,mode=tf.estimator.ModeKeys.TRAIN),max_steps = t_max_steps)



 eval_spec = tf.estimator.EvalSpec(input_fn = lambda : 

 make_input_fn(val_addrs,val_labels,

 e_batch_size,mode=tf.estimator.ModeKeys.EVAL),steps = 

 None,start_delay_secs=10,

    throttle_secs=300)





 tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)


Here are the log files:




INFO:tensorflow:Running training and evaluation locally
(non-distributed). INFO:tensorflow:Start train and evaluate loop. The
evaluate will happen after every checkpoint. Checkpoint frequency is
determined based on RunConfig arguments: save_checkpoints_steps None
or save_checkpoints_secs 300. WARNING:tensorflow:From
:9: map_and_batch (from
tensorflow.contrib.data.python.ops.batching) is deprecated and will be
removed in a future version. Instructions for updating: Use
tf.data.experimental.map_and_batch(...). WARNING:tensorflow:From
:12: shuffle_and_repeat (from
tensorflow.contrib.data.python.ops.shuffle_ops) is deprecated and will
be removed in a future version. Instructions for updating: Use
tf.data.experimental.shuffle_and_repeat(...). INFO:tensorflow:Calling
model_fn. INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings:
WarmStartSettings(ckpt_to_initialize_from='/content/training/keras/keras_model.ckpt',
vars_to_warm_start='.*', var_name_to_vocab_info={},
var_name_to_prev_var_name={}) INFO:tensorflow:Warm-starting from:
('/content/training/keras/keras_model.ckpt',)
INFO:tensorflow:Warm-starting variable: dense/kernel; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: dense/bias;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
dense_1/kernel; prev_var_name: Unchanged INFO:tensorflow:Warm-starting
variable: dense_1/bias; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: dense_2/kernel; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: dense_2/bias;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
Adam/iterations; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: Adam/lr; prev_var_name:
Unchanged INFO:tensorflow:Warm-starting variable: Adam/beta_1;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
Adam/beta_2; prev_var_name: Unchanged INFO:tensorflow:Warm-starting
variable: Adam/decay; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_1; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_2;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_3; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_4;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_5; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_6;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_7; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_8;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_9; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_10;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_11; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_12;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_13; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_14;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_15; prev_var_name: Unchanged
INFO:tensorflow:Warm-starting variable: training/Adam/Variable_16;
prev_var_name: Unchanged INFO:tensorflow:Warm-starting variable:
training/Adam/Variable_17; prev_var_name: Unchanged
INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was
finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done
running local_init_op. INFO:tensorflow:Saving checkpoints for 0 into
/content/training/model.ckpt. INFO:tensorflow:Saving checkpoints for 1
into /content/training/model.ckpt. INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting
evaluation at 2018-11-05-13:21:17 INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from
/content/training/model.ckpt-1 INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished
evaluation at 2018-11-05-13:22:08 INFO:tensorflow:Saving dict for
global step 1: acc = 0.0, global_step = 1, loss = 0.0
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1:
/content/training/model.ckpt-1 INFO:tensorflow:Loss for final step:
None.







python tensorflow deep-learning tensorflow-datasets tensorflow-estimator




python tensorflow deep-learning tensorflow-datasets tensorflow-estimator






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  • Welcome to SO, have you tried debugging and narrowing down the possible issues? At the moment it's close to "my code is broken, please fix", which is unlikely to attract answers. Try to figure out what you think is causing the accuracy and loss issues, and then edit this to include a specific question about a section of your code.
    – Shayn
    yesterday










  • I'm not able to figure out how I debug my code. Everything seems to be alright.
    – Aarya Patel
    2 hours ago


















  • Welcome to SO, have you tried debugging and narrowing down the possible issues? At the moment it's close to "my code is broken, please fix", which is unlikely to attract answers. Try to figure out what you think is causing the accuracy and loss issues, and then edit this to include a specific question about a section of your code.
    – Shayn
    yesterday










  • I'm not able to figure out how I debug my code. Everything seems to be alright.
    – Aarya Patel
    2 hours ago
















Welcome to SO, have you tried debugging and narrowing down the possible issues? At the moment it's close to "my code is broken, please fix", which is unlikely to attract answers. Try to figure out what you think is causing the accuracy and loss issues, and then edit this to include a specific question about a section of your code.
– Shayn
yesterday




Welcome to SO, have you tried debugging and narrowing down the possible issues? At the moment it's close to "my code is broken, please fix", which is unlikely to attract answers. Try to figure out what you think is causing the accuracy and loss issues, and then edit this to include a specific question about a section of your code.
– Shayn
yesterday












I'm not able to figure out how I debug my code. Everything seems to be alright.
– Aarya Patel
2 hours ago




I'm not able to figure out how I debug my code. Everything seems to be alright.
– Aarya Patel
2 hours ago

















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