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Saving and loading model weights

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目录

<!DOCTYPE html>

Coding Tutorial
In [3]:
import tensorflow as tf
print(tf.__version__)

2.0.0

Saving and loading models

Coding tutorials

1. Saving and loading model weights

2. Model saving criteria

3. Saving the entire model

4. Loading pre-trained Keras models

5. Tensorflow Hub modules

Saving and loading model weights

Load and inspect CIFAR-10 dataset

The CIFAR-10 dataset consists of, in total, 60000 color images, each with one of 10 labels: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. For an introduction and a download, see this link.

In [4]:
# Import the CIFAR-10 dataset and rescale the pixel values

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0

# Use smaller subset -- speeds things up
x_train = x_train[:10000]
y_train = y_train[:10000]
x_test = x_test[:1000]
y_test = y_test[:1000]

Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 3s 0us/step
In [5]:
# Plot the first 10 CIFAR-10 images

import matplotlib.pyplot as plt

fig, ax = plt.subplots(1, 10, figsize=(10, 1))
for i in range(10):
    ax[i].set_axis_off()
    ax[i].imshow(x_train[i])

Introduce two useful functions

In [6]:
# Introduce function to test model accuracy

def get_test_accuracy(model, x_test, y_test):
    test_loss, test_acc = model.evaluate(x=x_test, y=y_test, verbose=0)
    print('accuracy: {acc:0.3f}'.format(acc=test_acc))
In [7]:
# Introduce function that creates a new instance of a simple CNN

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D

def get_new_model():
    model = Sequential([
        Conv2D(filters=16, input_shape=(32, 32, 3), kernel_size=(3, 3), 
               activation='relu', name='conv_1'),
        Conv2D(filters=8, kernel_size=(3, 3), activation='relu', name='conv_2'),
        MaxPooling2D(pool_size=(4, 4), name='pool_1'),
        Flatten(name='flatten'),
        Dense(units=32, activation='relu', name='dense_1'),
        Dense(units=10, activation='softmax', name='dense_2')
    ])
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    return model

Create simple convolutional neural network classifier

In [9]:
# Create an instance of the model and show model summary
model = get_new_model()
model.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv_1 (Conv2D)              (None, 30, 30, 16)        448       
_________________________________________________________________
conv_2 (Conv2D)              (None, 28, 28, 8)         1160      
_________________________________________________________________
pool_1 (MaxPooling2D)        (None, 7, 7, 8)           0         
_________________________________________________________________
flatten (Flatten)            (None, 392)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 32)                12576     
_________________________________________________________________
dense_2 (Dense)              (None, 10)                330       
=================================================================
Total params: 14,514
Trainable params: 14,514
Non-trainable params: 0
_________________________________________________________________
In [10]:
get_test_accuracy(model, x_test, y_test)

accuracy: 0.085

Train model with checkpoints

In [11]:
from tensorflow.keras.callbacks import ModelCheckpoint
In [12]:
# Create Tensorflow checkpoint object
checkpoint_path = "model_checkpoints/checkpoint"
checkpoint = ModelCheckpoint(
    filepath=checkpoint_path,
    frequency = "epoch",
    save_weights_only = True,
    verbose=1
)
In [13]:
# Fit model, with simple checkpoint which saves (and overwrites) model weights every epoch
model.fit(x=x_train,
         y = y_train,
         epochs = 3,
         callbacks=[checkpoint])

Train on 10000 samples
Epoch 1/3
 9984/10000 [============================>.] - ETA: 0s - loss: 1.9953 - accuracy: 0.2550
Epoch 00001: saving model to model_checkpoints/checkpoint
10000/10000 [==============================] - 49s 5ms/sample - loss: 1.9949 - accuracy: 0.2551
Epoch 2/3
 9984/10000 [============================>.] - ETA: 0s - loss: 1.6323 - accuracy: 0.4099
Epoch 00002: saving model to model_checkpoints/checkpoint
10000/10000 [==============================] - 47s 5ms/sample - loss: 1.6319 - accuracy: 0.4099
Epoch 3/3
 9984/10000 [============================>.] - ETA: 0s - loss: 1.5014 - accuracy: 0.4566
Epoch 00003: saving model to model_checkpoints/checkpoint
10000/10000 [==============================] - 48s 5ms/sample - loss: 1.5010 - accuracy: 0.4568
Out[13]:
<tensorflow.python.keras.callbacks.History at 0x7f34c4405c50>
In [14]:
# Have a look at what the checkpoint creates

!ls -lh model_checkpoints

total 184K

-rw-r--r-- 1 jovyan users   77 Jan 23 15:00 checkpoint

-rw-r--r-- 1 jovyan users 174K Jan 23 15:00 checkpoint.data-00000-of-00001

-rw-r--r-- 1 jovyan users 2.0K Jan 23 15:00 checkpoint.index
In [15]:
# Evaluate the performance of the trained model

get_test_accuracy(model, x_test, y_test)

accuracy: 0.458

Create new model, load weights

In [16]:
# Create a new instance of the (initialised) model, accuracy around 10% again
model = get_new_model()
get_test_accuracy(model, x_test, y_test)

accuracy: 0.115
In [18]:
# Load weights -- accuracy is the same as the trained model
model.load_weights(checkpoint_path)
get_test_accuracy(model, x_test, y_test)

accuracy: 0.458

Clear directory

In [19]:
! rm -r model_checkpoints

Model saving criteria

Create more customised checkpoint

In [1]:
from tensorflow.keras.callbacks import ModelCheckpoint
In [11]:
# Create Tensorflow checkpoint object with epoch and batch details
checkpotint_5000_path = "model_checkpoints_5000/checkpoint_{epoch:02d}_{batch:04d}"
checkpoint_5000 = ModelCheckpoint(
    filepath= checkpotint_5000_path,
    save_weights_only=True,
    save_freq=5000,
    verbose=1
)
In [12]:
# Create and fit model with checkpoint
model = get_new_model()
model.fit(x=x_train,
          y=y_train,
         validation_data=(x_test, y_test),
         callbacks=[checkpoint_5000],
         epochs=3,
         batch_size=10
          )

Train on 10000 samples, validate on 1000 samples
Epoch 1/3
 4980/10000 [=============>................] - ETA: 25s - loss: 2.0374 - accuracy: 0.2508
Epoch 00001: saving model to model_checkpoints_5000/checkpoint_01_0499
 9990/10000 [============================>.] - ETA: 0s - loss: 1.8648 - accuracy: 0.3156
Epoch 00001: saving model to model_checkpoints_5000/checkpoint_01_0999
10000/10000 [==============================] - 53s 5ms/sample - loss: 1.8646 - accuracy: 0.3157 - val_loss: 1.6184 - val_accuracy: 0.4210
Epoch 2/3
 4980/10000 [=============>................] - ETA: 24s - loss: 1.5487 - accuracy: 0.4355
Epoch 00002: saving model to model_checkpoints_5000/checkpoint_02_0499
 9980/10000 [============================>.] - ETA: 0s - loss: 1.5080 - accuracy: 0.4497
Epoch 00002: saving model to model_checkpoints_5000/checkpoint_02_0999
10000/10000 [==============================] - 52s 5ms/sample - loss: 1.5074 - accuracy: 0.4500 - val_loss: 1.4468 - val_accuracy: 0.4930
Epoch 3/3
 4980/10000 [=============>................] - ETA: 24s - loss: 1.4108 - accuracy: 0.4920
Epoch 00003: saving model to model_checkpoints_5000/checkpoint_03_0499
 9990/10000 [============================>.] - ETA: 0s - loss: 1.3837 - accuracy: 0.5013
Epoch 00003: saving model to model_checkpoints_5000/checkpoint_03_0999
10000/10000 [==============================] - 51s 5ms/sample - loss: 1.3837 - accuracy: 0.5014 - val_loss: 1.3866 - val_accuracy: 0.5060
Out[12]:
<tensorflow.python.keras.callbacks.History at 0x7fc6f86fd630>
In [13]:
# Have a look at what the checkpoint creates

!ls -lh model_checkpoints_5000

total 1.1M

-rw-r--r-- 1 jovyan users   93 Jan 24 02:34 checkpoint

-rw-r--r-- 1 jovyan users 174K Jan 24 02:31 checkpoint_01_0499.data-00000-of-00001

-rw-r--r-- 1 jovyan users 2.0K Jan 24 02:31 checkpoint_01_0499.index

-rw-r--r-- 1 jovyan users 174K Jan 24 02:32 checkpoint_01_0999.data-00000-of-00001

-rw-r--r-- 1 jovyan users 2.0K Jan 24 02:32 checkpoint_01_0999.index

-rw-r--r-- 1 jovyan users 174K Jan 24 02:32 checkpoint_02_0499.data-00000-of-00001

-rw-r--r-- 1 jovyan users 2.0K Jan 24 02:32 checkpoint_02_0499.index

-rw-r--r-- 1 jovyan users 174K Jan 24 02:33 checkpoint_02_0999.data-00000-of-00001

-rw-r--r-- 1 jovyan users 2.0K Jan 24 02:33 checkpoint_02_0999.index

-rw-r--r-- 1 jovyan users 174K Jan 24 02:33 checkpoint_03_0499.data-00000-of-00001

-rw-r--r-- 1 jovyan users 2.0K Jan 24 02:33 checkpoint_03_0499.index

-rw-r--r-- 1 jovyan users 174K Jan 24 02:34 checkpoint_03_0999.data-00000-of-00001

-rw-r--r-- 1 jovyan users 2.0K Jan 24 02:34 checkpoint_03_0999.index

Work with model saving criteria

In [14]:
# Use tiny training and test set -- will overfit!

x_train = x_train[:100]
y_train = y_train[:100]
x_test = x_test[:100]
y_test = y_test[:100]
In [15]:
# Create a new instance of untrained model
model = get_new_model()
In [17]:
# Create Tensorflow checkpoint object which monitors the validation accuracy
checkpoint_best_path = "model_checkpoint_best/checkpoint"
checkpoint_best = ModelCheckpoint(
    filepath=checkpoint_best_path,
    save_weights_only=True,
    save_freq="epoch",
    monitor="val_accuracy",
    save_best_only=True,
    verbose=1
)
In [18]:
# Fit the model and save only the weights with the highest validation accuracy

history = model.fit(
    x=x_train,
    y=y_train,
    validation_data=(x_test, y_test),
    epochs= 50,
    batch_size = 10,
    verbose=0,
    callbacks=[checkpoint_best]
)

Epoch 00001: val_accuracy improved from -inf to 0.08000, saving model to model_checkpoint_best/checkpoint

Epoch 00002: val_accuracy did not improve from 0.08000

Epoch 00003: val_accuracy improved from 0.08000 to 0.10000, saving model to model_checkpoint_best/checkpoint

Epoch 00004: val_accuracy did not improve from 0.10000

Epoch 00005: val_accuracy did not improve from 0.10000

Epoch 00006: val_accuracy did not improve from 0.10000

Epoch 00007: val_accuracy did not improve from 0.10000

Epoch 00008: val_accuracy did not improve from 0.10000

Epoch 00009: val_accuracy improved from 0.10000 to 0.13000, saving model to model_checkpoint_best/checkpoint

Epoch 00010: val_accuracy improved from 0.13000 to 0.16000, saving model to model_checkpoint_best/checkpoint

Epoch 00011: val_accuracy did not improve from 0.16000

Epoch 00012: val_accuracy did not improve from 0.16000

Epoch 00013: val_accuracy did not improve from 0.16000

Epoch 00014: val_accuracy did not improve from 0.16000

Epoch 00015: val_accuracy improved from 0.16000 to 0.17000, saving model to model_checkpoint_best/checkpoint

Epoch 00016: val_accuracy improved from 0.17000 to 0.18000, saving model to model_checkpoint_best/checkpoint

Epoch 00017: val_accuracy did not improve from 0.18000

Epoch 00018: val_accuracy improved from 0.18000 to 0.20000, saving model to model_checkpoint_best/checkpoint

Epoch 00019: val_accuracy improved from 0.20000 to 0.22000, saving model to model_checkpoint_best/checkpoint

Epoch 00020: val_accuracy did not improve from 0.22000

Epoch 00021: val_accuracy did not improve from 0.22000

Epoch 00022: val_accuracy did not improve from 0.22000

Epoch 00023: val_accuracy did not improve from 0.22000

Epoch 00024: val_accuracy did not improve from 0.22000

Epoch 00025: val_accuracy did not improve from 0.22000

Epoch 00026: val_accuracy did not improve from 0.22000

Epoch 00027: val_accuracy did not improve from 0.22000

Epoch 00028: val_accuracy did not improve from 0.22000

Epoch 00029: val_accuracy did not improve from 0.22000

Epoch 00030: val_accuracy did not improve from 0.22000

Epoch 00031: val_accuracy did not improve from 0.22000

Epoch 00032: val_accuracy did not improve from 0.22000

Epoch 00033: val_accuracy did not improve from 0.22000

Epoch 00034: val_accuracy did not improve from 0.22000

Epoch 00035: val_accuracy did not improve from 0.22000

Epoch 00036: val_accuracy did not improve from 0.22000

Epoch 00037: val_accuracy did not improve from 0.22000

Epoch 00038: val_accuracy did not improve from 0.22000

Epoch 00039: val_accuracy did not improve from 0.22000

Epoch 00040: val_accuracy did not improve from 0.22000

Epoch 00041: val_accuracy did not improve from 0.22000

Epoch 00042: val_accuracy did not improve from 0.22000

Epoch 00043: val_accuracy did not improve from 0.22000

Epoch 00044: val_accuracy did not improve from 0.22000

Epoch 00045: val_accuracy did not improve from 0.22000

Epoch 00046: val_accuracy did not improve from 0.22000

Epoch 00047: val_accuracy did not improve from 0.22000

Epoch 00048: val_accuracy did not improve from 0.22000

Epoch 00049: val_accuracy did not improve from 0.22000

Epoch 00050: val_accuracy did not improve from 0.22000
In [19]:
# Plot training and testing curves

import pandas as pd

df = pd.DataFrame(history.history)
df.plot(y=['accuracy', 'val_accuracy'])
Out[19]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc694523588>
In [22]:
# Inspect the checkpoint directory

!ls -lh model_checkpoint_best

total 184K

-rw-r--r-- 1 jovyan users   77 Jan 24 02:56 checkpoint

-rw-r--r-- 1 jovyan users 174K Jan 24 02:56 checkpoint.data-00000-of-00001

-rw-r--r-- 1 jovyan users 2.0K Jan 24 02:56 checkpoint.index
In [25]:
# Create a new model with the saved weights
model = get_new_model()
model.load_weights(checkpoint_best_path)
get_test_accuracy(model,x_test, y_test)

accuracy: 0.220

Clear directory

In [ ]:
! rm -r model_checkpoint_5000 model_checkpoint_best

Saving the entire model

Create checkpoint that saves whole model, not just weights

In [29]:
from tensorflow.keras.callbacks import ModelCheckpoint
In [30]:
# Create Tensorflow checkpoint object
checkpoint_path = "model_checkpoints"
checkpoint = ModelCheckpoint(filepath=checkpoint_path,
                             save_weights_only=False,
                             verbose = 1,
                             frequency="epoch"
                            )
In [31]:
# Create and fit model with checkpoint
model = get_new_model()
model.fit(x=x_train,
         y=y_train,
         epochs=3,
         callbacks=[checkpoint])

Train on 100 samples
Epoch 1/3
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.learning_rate
WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.learning_rate
WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.
 96/100 [===========================>..] - ETA: 0s - loss: 2.3185 - accuracy: 0.1458
Epoch 00001: saving model to model_checkpoints
WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
INFO:tensorflow:Assets written to: model_checkpoints/assets
100/100 [==============================] - 3s 28ms/sample - loss: 2.3143 - accuracy: 0.1500
Epoch 2/3
 96/100 [===========================>..] - ETA: 0s - loss: 2.2768 - accuracy: 0.1771
Epoch 00002: saving model to model_checkpoints
INFO:tensorflow:Assets written to: model_checkpoints/assets
100/100 [==============================] - 2s 16ms/sample - loss: 2.2774 - accuracy: 0.1700
Epoch 3/3
 96/100 [===========================>..] - ETA: 0s - loss: 2.2561 - accuracy: 0.1667
Epoch 00003: saving model to model_checkpoints
INFO:tensorflow:Assets written to: model_checkpoints/assets
100/100 [==============================] - 2s 18ms/sample - loss: 2.2633 - accuracy: 0.1600
Out[31]:
<tensorflow.python.keras.callbacks.History at 0x7fc674716c50>

Inspect what the checkpoint has created

In [ ]:
# Have a look at what the checkpoint creates

!ls -lh model_checkpoints
In [33]:
# Enter variables directory

!ls -lh model_checkpoints/variables

total 184K

-rw-r--r-- 1 jovyan users 177K Jan 24 04:03 variables.data-00000-of-00001

-rw-r--r-- 1 jovyan users 2.1K Jan 24 04:03 variables.index
In [35]:
# Get the model's test accuracy
get_test_accuracy(model, x_test, y_test)

accuracy: 0.060

Create new model from scratch

In [36]:
# Delete model

del model
In [38]:
from tensorflow.keras.models import load_model
In [39]:
# Reload model from scratch
model = load_model(checkpoint_path)
get_test_accuracy(model, x_test, y_test)

accuracy: 0.060

Use the .h5 format to save model

In [42]:
# Save the model in .h5 format
model.save("my_model.h5")
In [43]:
# Inspect .h5 file
!ls -lh my_model.h5

-rw-r--r-- 1 jovyan users 77K Jan 24 04:14 my_model.h5
In [44]:
# Delete model

del model
model

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-44-d45022a59994> in <module>
      2 
      3 del model
----> 4 model

NameError: name 'model' is not defined
In [45]:
# Reload model from scratch
model = load_model("my_model.h5")
get_test_accuracy(model, x_test, y_test)

accuracy: 0.060

Clear directory

In [46]:
! rm -r model_checkpoints
! rm my_model.h5

Loading pre-trained Keras models

Import and build Keras ResNet50 model

Today we'll be using the ResNet50 model designed by a team at Microsoft Research, available through Keras applications. Please see the description on the Keras applications page for details. If you continue using it, please cite it properly! The paper it comes from is:

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition", 2015.

This model takes a long time to download on the Coursera platform, so it is pre-downloaded in your workspace and saved in Keras HDF5 format. If you want to import it on your personal machine, use the following code:

from tensorflow.keras.applications import ResNet50
model = ResNet50(weights='imagenet')

In this coding tutorial, you will instead load the model directly from disk.

In [4]:
from tensorflow.keras.models import load_model
In [8]:
# Build Keras ResNet50 model

model = load_model("models/Keras_ResNet50.h5")
model.summary()

WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.
Model: "resnet50"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    
__________________________________________________________________________________________________
conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  
__________________________________________________________________________________________________
conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 
__________________________________________________________________________________________________
conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   
__________________________________________________________________________________________________
pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 
__________________________________________________________________________________________________
pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  
__________________________________________________________________________________________________
conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 
__________________________________________________________________________________________________
conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 
__________________________________________________________________________________________________
conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          
                                                                 conv2_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           
__________________________________________________________________________________________________
conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           
__________________________________________________________________________________________________
conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           
                                                                 conv2_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           
__________________________________________________________________________________________________
conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           
__________________________________________________________________________________________________
conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           
                                                                 conv2_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           
__________________________________________________________________________________________________
conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           
__________________________________________________________________________________________________
conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           
__________________________________________________________________________________________________
conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          
                                                                 conv3_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           
__________________________________________________________________________________________________
conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           
__________________________________________________________________________________________________
conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           
                                                                 conv3_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           
__________________________________________________________________________________________________
conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           
__________________________________________________________________________________________________
conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           
                                                                 conv3_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           
__________________________________________________________________________________________________
conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           
__________________________________________________________________________________________________
conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           
                                                                 conv3_block4_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           
__________________________________________________________________________________________________
conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           
__________________________________________________________________________________________________
conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           
__________________________________________________________________________________________________
conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          
                                                                 conv4_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           
__________________________________________________________________________________________________
conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           
__________________________________________________________________________________________________
conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           
                                                                 conv4_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           
__________________________________________________________________________________________________
conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           
__________________________________________________________________________________________________
conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           
                                                                 conv4_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           
__________________________________________________________________________________________________
conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           
__________________________________________________________________________________________________
conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           
                                                                 conv4_block4_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           
__________________________________________________________________________________________________
conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           
__________________________________________________________________________________________________
conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           
                                                                 conv4_block5_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           
__________________________________________________________________________________________________
conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           
__________________________________________________________________________________________________
conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           
                                                                 conv4_block6_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           
__________________________________________________________________________________________________
conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           
__________________________________________________________________________________________________
conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           
__________________________________________________________________________________________________
conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          
                                                                 conv5_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           
__________________________________________________________________________________________________
conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           
__________________________________________________________________________________________________
conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           
                                                                 conv5_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           
__________________________________________________________________________________________________
conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           
__________________________________________________________________________________________________
conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           
                                                                 conv5_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           
__________________________________________________________________________________________________
avg_pool (GlobalAveragePooling2 (None, 2048)         0           conv5_block3_out[0][0]           
__________________________________________________________________________________________________
probs (Dense)                   (None, 1000)         2049000     avg_pool[0][0]                   
==================================================================================================
Total params: 25,636,712
Trainable params: 25,583,592
Non-trainable params: 53,120
__________________________________________________________________________________________________

Import and preprocess 3 sample images

In [9]:
# Import 3 sample ImageNet images

from tensorflow.keras.preprocessing.image import load_img

lemon_img = load_img('data/lemon.jpg', target_size=(224, 224))
viaduct_img = load_img('data/viaduct.jpg', target_size=(224, 224))
water_tower_img = load_img('data/water_tower.jpg', target_size=(224, 224))

Use ResNet50 model to classify images

In [10]:
# Useful function: presents top 5 predictions and probabilities

from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
import pandas as pd

def get_top_5_predictions(img):
    x = img_to_array(img)[np.newaxis, ...]
    x = preprocess_input(x)
    preds = decode_predictions(model.predict(x), top=5)
    top_preds = pd.DataFrame(columns=['prediction', 'probability'],
                             index=np.arange(5)+1)
    for i in range(5):
        top_preds.loc[i+1, 'prediction'] = preds[0][i][1]
        top_preds.loc[i+1, 'probability'] = preds[0][i][2] 
    return top_preds
Image 1: lemon
In [11]:
# Display image

lemon_img
Out[11]:
In [12]:
# Display top 5 predictions

get_top_5_predictions(lemon_img)

Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json
40960/35363 [==================================] - 0s 0us/step
Out[12]:
prediction probability
1 consomme 0.227801
2 lemon 0.221758
3 eggnog 0.151335
4 ladle 0.0400883
5 spotlight 0.0291972
Image 2: viaduct
In [13]:
# Display image

viaduct_img
Out[13]:
In [14]:
# Display top 5 predictions

get_top_5_predictions(viaduct_img)
Out[14]:
prediction probability
1 vault 0.367951
2 prison 0.111742
3 viaduct 0.110738
4 window_screen 0.0882307
5 fire_screen 0.0206752
Image 3: water tower
In [15]:
# Display image

water_tower_img
Out[15]:
In [16]:
# Display top 5 predictions

get_top_5_predictions(water_tower_img)
Out[16]:
prediction probability
1 solar_dish 0.384681
2 ladle 0.196179
3 planetarium 0.116682
4 strainer 0.04999
5 jigsaw_puzzle 0.0219473

Tensorflow Hub modules

Import and build Tensorflow Hub MobileNet v1 model

Today we'll be using Google's MobileNet v1 model, available on Tensorflow Hub. Please see the description on the Tensorflow Hub page for details on it's architecture, how it's trained, and the reference. If you continue using it, please cite it properly! The paper it comes from is:

Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam: "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", 2017.

This model takes a long time to download on the Coursera platform, so it is pre-downloaded in your workspace and saved in Tensorflow SavedModel format. If you want to import it on your personal machine, use the following code:

module_url = "https://tfhub.dev/google/imagenet/mobilenet_v1_050_160/classification/4"
model = Sequential([hub.KerasLayer(module_url)])
model.build(input_shape=[None, 160, 160, 3])

In this coding tutorial, you will instead load the model directly from disk.

In [17]:
import tensorflow_hub as hub
from tensorflow.keras.models import load_model
In [19]:
# Build Google's Mobilenet v1 model

module = load_model("models/Tensorflow_MobileNet_v1")
model = tf.keras.models.Sequential(hub.KerasLayer(module))
model.build(input_shape=[None, 160, 160, 3])
model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
keras_layer (KerasLayer)     multiple                  1343049   
=================================================================
Total params: 1,343,049
Trainable params: 0
Non-trainable params: 1,343,049
_________________________________________________________________

Use MobileNet model to classify images

In [20]:
# Import and preprocess 3 sample ImageNet images

from tensorflow.keras.preprocessing.image import load_img

lemon_img = load_img("data/lemon.jpg", target_size=(160, 160))
viaduct_img = load_img("data/viaduct.jpg", target_size=(160, 160))
water_tower_img = load_img("data/water_tower.jpg", target_size=(160, 160))
In [21]:
# Read in categories text file

with open('data/imagenet_categories.txt') as txt_file:
    categories = txt_file.read().splitlines()
In [22]:
# Useful function: presents top 5 predictions

import pandas as pd

def get_top_5_predictions(img):
    x = img_to_array(img)[np.newaxis, ...] / 255.0
    preds = model.predict(x)
    top_preds = pd.DataFrame(columns=['prediction'],
                             index=np.arange(5)+1)
    sorted_index = np.argsort(-preds[0])
    for i in range(5):
        ith_pred = categories[sorted_index[i]]
        top_preds.loc[i+1, 'prediction'] = ith_pred
            
    return top_preds
Image 1: lemon
In [23]:
lemon_img
Out[23]:
In [24]:
get_top_5_predictions(lemon_img)
Out[24]:
prediction
1 shower cap
2 tray
3 candle
4 brassiere
5 African chameleon
Image 2: viaduct
In [25]:
viaduct_img
Out[25]:
In [26]:
get_top_5_predictions(viaduct_img)
Out[26]:
prediction
1 viaduct
2 pier
3 dam
4 prison
5 solar dish
Image 3: water tower
In [28]:
water_tower_img
Out[28]:
In [29]:
get_top_5_predictions(water_tower_img)
Out[29]:
prediction
1 solar dish
2 water tower
3 aircraft carrier
4 jigsaw puzzle
5 oxygen mask