model/inspect_pretrained_vgg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.vgg16 import VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size=(256, 256, 3)\n",
    "model = VGG16(include_top=False, weights='imagenet', input_shape=input_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 256, 256, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv1 = model.get_layer(name='block1_conv1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__call__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_activity_regularizer',\n",
       " '_add_inbound_node',\n",
       " '_add_variable_with_custom_getter',\n",
       " '_assert_input_compatibility',\n",
       " '_call_convention',\n",
       " '_call_fn_args',\n",
       " '_callable_losses',\n",
       " '_checkpoint_dependencies',\n",
       " '_compute_causal_padding',\n",
       " '_compute_previous_mask',\n",
       " '_convolution_op',\n",
       " '_deferred_dependencies',\n",
       " '_dtype',\n",
       " '_expects_training_arg',\n",
       " '_gather_saveables_for_checkpoint',\n",
       " '_get_node_attribute_at_index',\n",
       " '_handle_activity_regularization',\n",
       " '_handle_deferred_dependencies',\n",
       " '_handle_weight_regularization',\n",
       " '_in_call',\n",
       " '_inbound_nodes',\n",
       " '_init_set_name',\n",
       " '_initial_weights',\n",
       " '_inputs_from_call_args',\n",
       " '_lookup_dependency',\n",
       " '_losses',\n",
       " '_maybe_initialize_checkpointable',\n",
       " '_name',\n",
       " '_name_based_attribute_restore',\n",
       " '_name_based_restores',\n",
       " '_name_scope',\n",
       " '_no_dependency',\n",
       " '_non_trainable_weights',\n",
       " '_outbound_nodes',\n",
       " '_preload_simple_restoration',\n",
       " '_restore_from_checkpoint_position',\n",
       " '_set_connectivity_metadata_',\n",
       " '_set_learning_phase_metadata',\n",
       " '_set_mask_metadata',\n",
       " '_setattr_tracking',\n",
       " '_single_restoration_from_checkpoint_position',\n",
       " '_tf_api_names',\n",
       " '_tf_api_names_v1',\n",
       " '_track_checkpointable',\n",
       " '_trainable_weights',\n",
       " '_unconditional_checkpoint_dependencies',\n",
       " '_unconditional_deferred_dependencies',\n",
       " '_unconditional_dependency_names',\n",
       " '_update_uid',\n",
       " '_updates',\n",
       " 'activation',\n",
       " 'activity_regularizer',\n",
       " 'add_loss',\n",
       " 'add_update',\n",
       " 'add_variable',\n",
       " 'add_weight',\n",
       " 'apply',\n",
       " 'bias',\n",
       " 'bias_constraint',\n",
       " 'bias_initializer',\n",
       " 'bias_regularizer',\n",
       " 'build',\n",
       " 'built',\n",
       " 'call',\n",
       " 'compute_mask',\n",
       " 'compute_output_shape',\n",
       " 'count_params',\n",
       " 'data_format',\n",
       " 'dilation_rate',\n",
       " 'dtype',\n",
       " 'filters',\n",
       " 'from_config',\n",
       " 'get_config',\n",
       " 'get_input_at',\n",
       " 'get_input_mask_at',\n",
       " 'get_input_shape_at',\n",
       " 'get_losses_for',\n",
       " 'get_output_at',\n",
       " 'get_output_mask_at',\n",
       " 'get_output_shape_at',\n",
       " 'get_updates_for',\n",
       " 'get_weights',\n",
       " 'inbound_nodes',\n",
       " 'input',\n",
       " 'input_mask',\n",
       " 'input_shape',\n",
       " 'input_spec',\n",
       " 'kernel',\n",
       " 'kernel_constraint',\n",
       " 'kernel_initializer',\n",
       " 'kernel_regularizer',\n",
       " 'kernel_size',\n",
       " 'losses',\n",
       " 'name',\n",
       " 'non_trainable_variables',\n",
       " 'non_trainable_weights',\n",
       " 'outbound_nodes',\n",
       " 'output',\n",
       " 'output_mask',\n",
       " 'output_shape',\n",
       " 'padding',\n",
       " 'rank',\n",
       " 'set_weights',\n",
       " 'stateful',\n",
       " 'strides',\n",
       " 'supports_masking',\n",
       " 'trainable',\n",
       " 'trainable_variables',\n",
       " 'trainable_weights',\n",
       " 'updates',\n",
       " 'use_bias',\n",
       " 'variables',\n",
       " 'weights']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'block1_conv1',\n",
       " 'trainable': True,\n",
       " 'dtype': 'float32',\n",
       " 'filters': 64,\n",
       " 'kernel_size': (3, 3),\n",
       " 'strides': (1, 1),\n",
       " 'padding': 'same',\n",
       " 'data_format': 'channels_last',\n",
       " 'dilation_rate': (1, 1),\n",
       " 'activation': 'relu',\n",
       " 'use_bias': True,\n",
       " 'kernel_initializer': {'class_name': 'GlorotUniform',\n",
       "  'config': {'seed': None, 'dtype': 'float32'}},\n",
       " 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}},\n",
       " 'kernel_regularizer': None,\n",
       " 'bias_regularizer': None,\n",
       " 'activity_regularizer': None,\n",
       " 'kernel_constraint': None,\n",
       " 'bias_constraint': None}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv1.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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