In TF 2.2.0-rc1, validation_data expects tuples, not lists, fixes ageron#131

Author: ageron

14_deep_computer_vision_with_cnns.ipynb

@@ -837,7 +837,7 @@
    ],
    "source": [
     "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
-    "history = model.fit(X_train, y_train, epochs=10, validation_data=[X_valid, y_valid])\n",
+    "history = model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))\n",
     "score = model.evaluate(X_test, y_test)\n",
     "X_new = X_test[:10] # pretend we have new images\n",
     "y_pred = model.predict(X_new)"
@@ -2098,7 +2098,7 @@
     "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\",\n",
     "              metrics=[\"accuracy\"])\n",
     "\n",
-    "model.fit(X_train, y_train, epochs=10, validation_data=[X_valid, y_valid])\n",
+    "model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))\n",
     "model.evaluate(X_test, y_test)"
    ]
   },

17_autoencoders_and_gans.ipynb

@@ -408,7 +408,7 @@
     "stacked_ae.compile(loss=\"binary_crossentropy\",\n",
     "                   optimizer=keras.optimizers.SGD(lr=1.5), metrics=[rounded_accuracy])\n",
     "history = stacked_ae.fit(X_train, X_train, epochs=20,\n",
-    "                         validation_data=[X_valid, X_valid])"
+    "                         validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -655,7 +655,7 @@
     "tied_ae.compile(loss=\"binary_crossentropy\",\n",
     "                optimizer=keras.optimizers.SGD(lr=1.5), metrics=[rounded_accuracy])\n",
     "history = tied_ae.fit(X_train, X_train, epochs=10,\n",
-    "                      validation_data=[X_valid, X_valid])"
+    "                      validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -708,7 +708,7 @@
     "    autoencoder = keras.models.Sequential([encoder, decoder])\n",
     "    autoencoder.compile(optimizer, loss, metrics=metrics)\n",
     "    autoencoder.fit(X_train, X_train, epochs=n_epochs,\n",
-    "                    validation_data=[X_valid, X_valid])\n",
+    "                    validation_data=(X_valid, X_valid))\n",
     "    return encoder, decoder, encoder(X_train), encoder(X_valid)"
    ]
   },
@@ -855,7 +855,7 @@
     "stacked_ae_1_by_1.compile(loss=\"binary_crossentropy\",\n",
     "                          optimizer=keras.optimizers.SGD(lr=0.1), metrics=[rounded_accuracy])\n",
     "history = stacked_ae_1_by_1.fit(X_train, X_train, epochs=10,\n",
-    "                                validation_data=[X_valid, X_valid])"
+    "                                validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -943,7 +943,7 @@
     "conv_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n",
     "                metrics=[rounded_accuracy])\n",
     "history = conv_ae.fit(X_train, X_train, epochs=5,\n",
-    "                      validation_data=[X_valid, X_valid])"
+    "                      validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -1095,7 +1095,7 @@
     }
    ],
    "source": [
-    "history = recurrent_ae.fit(X_train, X_train, epochs=10, validation_data=[X_valid, X_valid])"
+    "history = recurrent_ae.fit(X_train, X_train, epochs=10, validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -1187,7 +1187,7 @@
     "denoising_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n",
     "                     metrics=[rounded_accuracy])\n",
     "history = denoising_ae.fit(X_train, X_train, epochs=10,\n",
-    "                           validation_data=[X_valid, X_valid])"
+    "                           validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -1276,7 +1276,7 @@
     "dropout_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n",
     "                   metrics=[rounded_accuracy])\n",
     "history = dropout_ae.fit(X_train, X_train, epochs=10,\n",
-    "                         validation_data=[X_valid, X_valid])"
+    "                         validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -1378,7 +1378,7 @@
     "simple_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.),\n",
     "                  metrics=[rounded_accuracy])\n",
     "history = simple_ae.fit(X_train, X_train, epochs=10,\n",
-    "                        validation_data=[X_valid, X_valid])"
+    "                        validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -1545,7 +1545,7 @@
     "sparse_l1_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n",
     "                     metrics=[rounded_accuracy])\n",
     "history = sparse_l1_ae.fit(X_train, X_train, epochs=10,\n",
-    "                           validation_data=[X_valid, X_valid])"
+    "                           validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -1715,7 +1715,7 @@
     "sparse_kl_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n",
     "              metrics=[rounded_accuracy])\n",
     "history = sparse_kl_ae.fit(X_train, X_train, epochs=10,\n",
-    "                           validation_data=[X_valid, X_valid])"
+    "                           validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -1886,7 +1886,7 @@
     "variational_ae.add_loss(K.mean(latent_loss) / 784.)\n",
     "variational_ae.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[rounded_accuracy])\n",
     "history = variational_ae.fit(X_train, X_train, epochs=25, batch_size=128,\n",
-    "                             validation_data=[X_valid, X_valid])"
+    "                             validation_data=(X_valid, X_valid))"
    ]
   },
   {
@@ -4304,7 +4304,7 @@
     "])\n",
     "classifier.compile(loss=\"sparse_categorical_crossentropy\", optimizer=keras.optimizers.SGD(lr=0.02),\n",
     "                   metrics=[\"accuracy\"])\n",
-    "history = classifier.fit(X_train_small, y_train_small, epochs=20, validation_data=[X_valid, y_valid])"
+    "history = classifier.fit(X_train_small, y_train_small, epochs=20, validation_data=(X_valid, y_valid))"
    ]
   },
   {
@@ -4429,7 +4429,7 @@
     "                       optimizer=keras.optimizers.SGD(lr=0.02),\n",
     "                       metrics=[\"accuracy\"])\n",
     "history = pretrained_clf.fit(X_train_small, y_train_small, epochs=30,\n",
-    "                             validation_data=[X_valid, y_valid])"
+    "                             validation_data=(X_valid, y_valid))"
    ]
   },
   {
@@ -4493,7 +4493,7 @@
     "                       optimizer=keras.optimizers.SGD(lr=0.02),\n",
     "                       metrics=[\"accuracy\"])\n",
     "history = pretrained_clf.fit(X_train_small, y_train_small, epochs=20,\n",
-    "                             validation_data=[X_valid, y_valid])"
+    "                             validation_data=(X_valid, y_valid))"
    ]
   },
   {
@@ -4555,7 +4555,7 @@
     "hashing_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n",
     "                   metrics=[rounded_accuracy])\n",
     "history = hashing_ae.fit(X_train, X_train, epochs=10,\n",
-    "                         validation_data=[X_valid, X_valid])"
+    "                         validation_data=(X_valid, X_valid))"
    ]
   },
   {